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Current Medical Imaging

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ISSN (Online): 1875-6603

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Systematic Review Article

Cancer Detection Based on Medical Image Analysis with the Help of Machine Learning and Deep Learning Techniques: A Systematic Literature Review

Author(s): Tamanna Sood*, Rajesh Bhatia and Padmavati Khandnor

Volume 19, Issue 13, 2023

Published on: 20 March, 2023

Article ID: e170223213746 Pages: 36

DOI: 10.2174/1573405619666230217100130

Price: $65

Abstract

Background: Cancer is a deadly disease. It is crucial to diagnose cancer in its early stages. This can be done with medical imaging. Medical imaging helps us scan and view internal organs. The analysis of these images is a very important task in the identification and classification of cancer. Over the past years, the occurrence of cancer has been increasing, so has been the load on the medical fraternity. Fortunately, with the growth of Artificial Intelligence in the past decade, many tools and techniques have emerged which may help doctors in the analysis of medical images.

Methodology: This is a systematic study covering various tools and techniques used for medical image analysis in the field of cancer detection. It focuses on machine learning and deep learning technologies, their performances, and their shortcomings. Also, the various types of imaging techniques and the different datasets used have been discussed extensively. This work also discusses the various preprocessing techniques that have been performed on medical images for better classification.

Results: A total of 270 studies from 5 different publications and 5 different conferences have been included and compared on the above-cited parameters.

Conclusion: Recommendations for future work have been given towards the end.

Graphical Abstract

[1]
World Health Organization (WHO). Cancer Today. 2020. Available from: https://gco.iarc.fr/today/data/factsheets/populations/356-india-fact-sheets
[2]
Webmed. Cancer Types. 2020. Available from: https://www.webmd.com/cancer/guide/cancer-guide-cancer-types
[3]
Webmed. Cancer Guide- Treatment and Care. 2020. Available from: https://www.webmd.com/cancer/guide/cancer-guide-treatment-care
[4]
Kitchenham B. Procedures for Performing Systematic Reviews, Version 1.0. Empir Softw Eng 2004; 33(2004): 1-26.
[5]
Ker J, Bai Y, Lee HY, Rao J, Wang L. Automated brain histology classification using machine learning. J Clin Neurosci 2019; 66: 239-45.
[http://dx.doi.org/10.1016/j.jocn.2019.05.019] [PMID: 31155342]
[6]
Rehman A, Naz S, Razzak MI, Akram F, Imran M. A deep learning-based framework for automatic brain tumors classification using transfer learning. Circuits Syst Signal Process 2020; 39(2): 757-75.
[http://dx.doi.org/10.1007/s00034-019-01246-3]
[7]
Sert E, Özyurt F, Doğantekin A. A new approach for brain tumor diagnosis system: Single image super resolution based maximum fuzzy entropy segmentation and convolutional neural network. Med Hypotheses 2019; 133: 109413.
[http://dx.doi.org/10.1016/j.mehy.2019.109413] [PMID: 31586812]
[8]
Abdelaziz Ismael SA, Mohammed A, Hefny H. An enhanced deep learning approach for brain cancer MRI images classification using residual networks. Artif Intell Med 2020; 102: 101779.
[http://dx.doi.org/10.1016/j.artmed.2019.101779]
[9]
Kumar Mallick P, Ryu SH, Satapathy SK, Mishra S, Nguyen GN, Tiwari P. Brain MRI image classification for cancer detection using deep wavelet autoencoder-based deep neural network. IEEE Access 2019; 7: 46278-87.
[http://dx.doi.org/10.1109/ACCESS.2019.2902252]
[10]
Mohsen H, El-Dahshan E-SA, El-Horbaty E-SM, Salem A-BM. Classification using deep learning neural networks for brain tumors. Future Comput Inform J 2018; 3(1): 68-71.
[http://dx.doi.org/10.1016/j.fcij.2017.12.001]
[11]
Mzoughi H, Njeh I, Wali A, et al. Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI gliomas brain tumor classification. J Digit Imaging 2020; 33(4): 903-15.
[http://dx.doi.org/10.1007/s10278-020-00347-9] [PMID: 32440926]
[12]
Lu S, Wang SH, Zhang YD. Detection of abnormal brain in MRI via improved AlexNet and ELM optimized by chaotic bat algorithm. Neural Comput Appl 2021; 33(17): 10799-811.
[http://dx.doi.org/10.1007/s00521-020-05082-4]
[13]
Han C. Combining noise-to-image and image-to-image GANs: Brain MR image augmentation for tumor detection. IEEE Access 2019; 7: 156966-77.
[http://dx.doi.org/10.1109/ACCESS.2019.2947606]
[14]
Gu Y. MedSRGAN: medical images super-resolution using generative adversarial networks. Multimedia Tools Appl 2020; 79(29–30): 21815-40.
[http://dx.doi.org/10.1007/s11042-020-08980-w]
[15]
Armanious K, Jiang C, Fischer M, et al. MedGAN: Medical image translation using GANs. Comput Med Imaging Graph 2020; 79: 101684.
[http://dx.doi.org/10.1016/j.compmedimag.2019.101684] [PMID: 31812132]
[16]
Xie F, Fan H, Li Y, Jiang Z, Meng R, Bovik A. Melanoma classification on dermoscopy images using a neural network ensemble model. IEEE Trans Med Imaging 2017; 36(3): 849-58.
[http://dx.doi.org/10.1109/TMI.2016.2633551] [PMID: 27913337]
[17]
Xu Q, Wang X, Jiang H. Convolutional neural network for breast cancer diagnosis using diffuse optical tomography. Vis Comput Ind Biomed Art 2019; 2(1): 1-6.
[http://dx.doi.org/10.1186/s42492-019-0012-y] [PMID: 32240400]
[18]
Brancati N, De Pietro G, Frucci M, Riccio D. A deep learning approach for breast invasive ductal carcinoma detection and lymphoma multi-classification in histological images. IEEE Access 2019; 7: 44709-20.
[19]
Burçak KC, Baykan ÖK, Uğuz H. A new deep convolutional neural network model for classifying breast cancer histopathological images and the hyperparameter optimisation of the proposed model. J Supercomput 2021; 77(1): 973-89.
[http://dx.doi.org/10.1007/s11227-020-03321-y]
[20]
Gupta K, Chawla N. Analysis of histopathological images for prediction of breast cancer using traditional classifiers with pretrained CNN Proc Comput Sci 2020; 167: 878-89.
[21]
Singla N, Dubey K, Srivastava V. Automated assessment of breast cancer margin in optical coherence tomography images via pretrained convolutional neural network. J Biophotonics 2019; 12(3): e201800255.
[http://dx.doi.org/10.1002/jbio.201800255] [PMID: 30318761]
[22]
Aresta G, Araújo T, Kwok S, et al. BACH: Grand challenge on breast cancer histology images. Med Image Anal 2019; 56: 122-39.
[http://dx.doi.org/10.1016/j.media.2019.05.010] [PMID: 31226662]
[23]
Alom MZ, Yakopcic C, Nasrin MS, Taha TM, Asari VK. Breast cancer classification from histopathological images with inception recurrent residual convolutional neural network. J Digit Imaging 2019; 32(4): 605-17.
[http://dx.doi.org/10.1007/s10278-019-00182-7] [PMID: 30756265]
[24]
Vaka A R, Soni B. Breast cancer detection by leveraging Machine Learning. ICT Express 2020; 6(4): 320-4.
[http://dx.doi.org/10.1016/j.icte.2020.04.009]
[25]
Wang Y. Breast cancer image classification via multi-network features and dual-network orthogonal low-rank learning. IEEE Access 2020; 8: 27779-92.
[http://dx.doi.org/10.1109/ACCESS.2020.2964276]
[26]
Murtaza G, Shuib L, Mujtaba G, Raza G. Breast cancer multi-classification through deep neural network and hierarchical classification approach. Multimedia Tools Appl 2020; 79(21–22): 15481-511.
[http://dx.doi.org/10.1007/s11042-019-7525-4]
[27]
Toğaçar, M., Özkurt, K.B., Ergen, B. and Cömert, Z., 2020. BreastNet: A novel convolutional neural network model through histopathological images for the diagnosis of breast cancer. Physica A: Statistical Mechanics and its Applications 545: 123592.
[http://dx.doi.org/10.1016/j.physa.2019.123592]
[28]
Dabeer, S., Khan, M.M. and Islam, S., 2019. Cancer diagnosis in histopathological image: CNN based approach. Informatics in Medicine Unlocked 16: p. 100231.
[http://dx.doi.org/10.1016/j.imu.2019.100231]
[29]
Bardou D, Zhang K, Ahmad SM. Classification of breast cancer based on histology images using convolutional neural networks. IEEE Access 2018; 6: 24680-93.
[http://dx.doi.org/10.1109/ACCESS.2018.2831280]
[30]
Vo DM, Nguyen NQ, Lee SW. Classification of breast cancer histology images using incremental boosting convolution networks. Inf Sci (Ny) 2019; 482: 123-38.
[http://dx.doi.org/10.1016/j.ins.2018.12.089]
[31]
Li Y, Wu J, Wu Q. Classification of breast cancer histology images using multi-size and discriminative patches based on deep learning. IEEE Access 2019; 7: 21400-8.
[http://dx.doi.org/10.1109/ACCESS.2019.2898044]
[32]
Budak Ü, Cömert Z, Rashid ZN, Şengür A, Çıbuk M. Computer-aided diagnosis system combining FCN and Bi-LSTM model for efficient breast cancer detection from histopathological images. Appl Soft Comput J 2019; 85: 105765.
[http://dx.doi.org/10.1016/j.asoc.2019.105765]
[33]
Feng Y, Zhang L, Mo J. Deep manifold preserving autoencoder for classifying breast cancer histopathological images. IEEE/ACM Trans Comput Biol Bioinformatics 2020; 17(1): 91-101.
[http://dx.doi.org/10.1109/TCBB.2018.2858763] [PMID: 30040652]
[34]
Gecer B, Aksoy S, Mercan E, Shapiro LG, Weaver DL, Elmore JG. Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks. Pattern Recogn 2018; 84: 345-56.
[http://dx.doi.org/10.1016/j.patcog.2018.07.022] [PMID: 30679879]
[35]
Murtaza G, Shuib L, Wahab AWA, Mujtaba G, Raza G. Ensembled deep convolution neural network-based breast cancer classification with misclassification reduction algorithms. Multimedia Tools Appl 2020; 79(25–26): 18447-79.
[http://dx.doi.org/10.1007/s11042-020-08692-1]
[36]
Kausar T, Wang MJ, Idrees M, Lu Y. HWDCNN: Multi-class recognition in breast histopathology with Haar wavelet decomposed image based convolution neural network. Biocybern Biomed Eng 2019; 39(4): 967-82.
[http://dx.doi.org/10.1016/j.bbe.2019.09.003]
[37]
Gandomkar Z, Brennan PC, Mello-Thoms C. MuDeRN: Multi-category classification of breast histopathological image using deep residual networks. Artif Intell Med 2018; 88: 14-24.
[http://dx.doi.org/10.1016/j.artmed.2018.04.005] [PMID: 29705552]
[38]
Roy, K., Banik, D., Bhattacharjee, D. and Nasipuri, M., 2019. Patch-based system for classification of breast histology images using deep learning. Computerized Medical Imaging and Graphics 71: pp. 90-103.
[http://dx.doi.org/10.1016/j.compmedimag.2018.11.003]
[39]
Saxena S, Shukla S, Gyanchandani M. Pre-trained convolutional neural networks as feature extractors for diagnosis of breast cancer using histopathology. Int J Imaging Syst Technol 2020; 30(3): 577-91.
[http://dx.doi.org/10.1002/ima.22399]
[40]
Deniz E, Şengür A, Kadiroğlu Z, Guo Y, Bajaj V, Budak Ü. Transfer learning based histopathologic image classification for breast cancer detection. Health Inf Sci Syst 2018; 6(1): 18.
[http://dx.doi.org/10.1007/s13755-018-0057-x] [PMID: 30279988]
[41]
Benhammou Y, Achchab B, Herrera F, Tabik S. BreakHis based breast cancer automatic diagnosis using deep learning: Taxonomy, survey and insights. Neurocomputing 2020; 375: 9-24.
[http://dx.doi.org/10.1016/j.neucom.2019.09.044]
[42]
Chang, J., Yu, J., Han, T., Chang, H.J. and Park, E., 2017, October. A method for classifying medical images using transfer learning: A pilot study on histopathology of breast cancer. In 2017 IEEE 19th international conference on e-health networking, applications and services (Healthcom). (pp. 1-4). IEEE.
[43]
Guo Y, Song Q, Jiang M, et al. Histological subtypes classification of lung cancers on CT images using 3d deep learning and radiomics. Acad Radiol 2021; 28(9): e258-66.
[http://dx.doi.org/10.1016/j.acra.2020.06.010] [PMID: 32622740]
[44]
Saranyaraj D, Manikandan M, Maheswari S. A deep convolutional neural network for the early detection of breast carcinoma with respect to hyper- parameter tuning. Multimedia Tools Appl 2020; 79(15-16): 11013-38.
[http://dx.doi.org/10.1007/s11042-018-6560-x]
[45]
Al-Antari MA, Al-Masni MA, Choi MT, Han SM, Kim TS. A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. Int J Med Inform 2018; 117(April): 44-54.
[http://dx.doi.org/10.1016/j.ijmedinf.2018.06.003] [PMID: 30032964]
[46]
Mihaylov I, Kańduła M, Krachunov M, Vassilev D. A novel framework for horizontal and vertical data integration in cancer studies with application to survival time prediction models. Biol Direct 2019; 14(1): 22.
[http://dx.doi.org/10.1186/s13062-019-0249-6] [PMID: 31752974]
[47]
Zhang YD, Pan C, Chen X, Wang F. Abnormal breast identification by nine-layer convolutional neural network with parametric rectified linear unit and rank-based stochastic pooling. J Comput Sci 2018; 27: 57-68.
[http://dx.doi.org/10.1016/j.jocs.2018.05.005]
[48]
Al-antari MA. An automatic computer-aided diagnosis system for breast cancer in digital mammograms via deep belief network. J Med Biol Eng 2018; 38(3): 443-56.
[http://dx.doi.org/10.1007/s40846-017-0321-6]
[49]
Wang Z. Breast cancer detection using extreme learning machine based on feature fusion with CNN deep features. IEEE Access 2019; 7: 105146-58.
[http://dx.doi.org/10.1109/ACCESS.2019.2892795]
[50]
Wang H. Breast mass classification via deeply integrating the contextual information from multi-view data. Pattern Recogn 2018; 80: 42-52.
[http://dx.doi.org/10.1016/j.patcog.2018.02.026]
[51]
Perek S, Kiryati N, Zimmerman-Moreno G, Sklair-Levy M, Konen E, Mayer A. Classification of contrast-enhanced spectral mammography (CESM) images. Int J CARS 2019; 14(2): 249-57.
[http://dx.doi.org/10.1007/s11548-018-1876-6] [PMID: 30367322]
[52]
Agnes SA, Anitha J, Pandian SIA, Peter JD. Classification of mammogram images using multiscale all convolutional neural network (MA-CNN). J Med Syst 2019; 44(1): 30.
[http://dx.doi.org/10.1007/s10916-019-1494-z] [PMID: 31838610]
[53]
Zhang X, Zhang Y, Han EY, et al. Classification of whole mammogram and tomosynthesis images using deep convolutional neural networks. IEEE Trans Nanobiosci 2018; 17(3): 237-42.
[http://dx.doi.org/10.1109/TNB.2018.2845103] [PMID: 29994219]
[54]
Cai G, Guo Y, Chen W, Zeng H, Zhou Y, Lu Y. Computer-aided detection and diagnosis of microcalcification clusters on full field digital mammograms based on deep learning method using neutrosophic boosting. Multimedia Tools Appl 2020; 79(23-24): 17147-67.
[http://dx.doi.org/10.1007/s11042-019-7726-x]
[55]
Arora R, Rai PK, Raman B. Deep feature-based automatic classification of mammograms. Med Biol Eng Comput 2020; 58(6): 1199-211.
[http://dx.doi.org/10.1007/s11517-020-02150-8] [PMID: 32200453]
[56]
Al-Antari MA, Han SM, Kim TS. Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms. Comput Methods Programs Biomed 2020; 196: 105584.
[http://dx.doi.org/10.1016/j.cmpb.2020.105584] [PMID: 32554139]
[57]
Mabrouk MS, Afify HM, Marzouk SY. Fully automated computer-aided diagnosis system for micro calcifications cancer based on improved mammographic image techniques. Ain Shams Eng J 2019; 10(3): 517-27.
[http://dx.doi.org/10.1016/j.asej.2019.01.009]
[58]
Kaur P, Singh G, Kaur P. Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification. Informatics Med Unlocked 2019; 16: 100151.
[http://dx.doi.org/10.1016/j.imu.2019.01.001]
[59]
Teare P, Fishman M, Benzaquen O, Toledano E, Elnekave E. Malignancy detection on mammography using dual deep convolutional neural networks and genetically discovered false color input enhancement. J Digit Imaging 2017; 30(4): 499-505.
[http://dx.doi.org/10.1007/s10278-017-9993-2] [PMID: 28656455]
[60]
Song R, Li T, Wang Y. Mammographic classification based on XGBoost and DCNN with multi features. IEEE Access 2020; 8: 75011-21.
[http://dx.doi.org/10.1109/ACCESS.2020.2986546]
[61]
Samala RK, Chan HP, Hadjiiski L, Helvie MA, Wei J, Cha K. Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography. Med Phys 2016; 43(12): 6654-66.
[http://dx.doi.org/10.1118/1.4967345] [PMID: 27908154]
[62]
Nasir Khan H, Shahid AR, Raza B, Dar AH, Alquhayz H. Multiview feature fusion based four views model for mammogram classification using convolutional neural network. IEEE Access 2019; 7: 165724-33.
[http://dx.doi.org/10.1109/ACCESS.2019.2953318]
[63]
Gao, F., Wu, T., Li, J., Zheng, B., Ruan, L., Shang, D. and Patel, B., 2018. SD-CNN: A shallow-deep CNN for improved breast cancer diagnosis. Computerized Medical Imaging and Graphics 70: pp. 53-62.
[http://dx.doi.org/10.1016/j.compmedimag.2018.09.004]
[64]
Al-Masni MA, Al-Antari MA, Park JM, et al. Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system. Comput Methods Programs Biomed 2018; 157: 85-94.
[http://dx.doi.org/10.1016/j.cmpb.2018.01.017] [PMID: 29477437]
[65]
Pardamean B, Cenggoro TW, Rahutomo R, Budiarto A, Karuppiah EK. Transfer learning from chest X-ray pre-trained convolutional neural network for learning mammogram data. Procedia Comput Sci 2018; 135: 400-7.
[http://dx.doi.org/10.1016/j.procs.2018.08.190]
[66]
De Yu S, Liu LL, Wang ZY, Dai GZ, Xie YQ. Transferring deep neural networks for the differentiation of mammographic breast lesions. Sci China Technol Sci 2019; 62(3): 441-7.
[http://dx.doi.org/10.1007/s11431-017-9317-3]
[67]
Yu X, Zeng N, Liu S, Zhang YD. Utilization of DenseNet201 for diagnosis of breast abnormality. Mach Vis Appl 2019; 30(7–8): 1135-44.
[http://dx.doi.org/10.1007/s00138-019-01042-8]
[68]
Vijayarajan SM, Jaganathan P. A novel comparative study on breast cancer detection using different types of classification techniques. Concurr Comput Pract Exp 2019; 31(14): 1-12.
[http://dx.doi.org/10.1002/cpe.4939]
[69]
Kim HE, Kim HH, Han BK, et al. Changes in cancer detection and false-positive recall in mammography using artificial intelligence: A retrospective, multireader study. Lancet Digit Health 2020; 2(3): e138-48.
[http://dx.doi.org/10.1016/S2589-7500(20)30003-0] [PMID: 33334578]
[70]
Abdelhafiz D, Yang C, Ammar R, Nabavi S. Deep convolutional neural networks for mammography: advances, challenges and applications. BMC bioinformatics. 2019; 20: pp. 1-20.
[http://dx.doi.org/10.1186/s12859-019-2823-4]
[71]
Azar AT, El-Said SA. Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Comput Appl 2014; 24(5): 1163-77.
[http://dx.doi.org/10.1007/s00521-012-1324-4]
[72]
Singh VK. Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network. Expert Syst Appl 2020; 139: 112855.
[http://dx.doi.org/10.1016/j.eswa.2019.112855]
[73]
Shams S, Platania R, Zhang J, Kim J. Deep generative breast cancer screening and diagnosis. Springer International Publishing 2018.
[http://dx.doi.org/10.1007/978-3-030-00934-2_95]
[74]
Sainz de Cea, M.V., Diedrich, K., Bakalo, R., Ness, L. and, Richmond, D., 2020. Multi-task learning for detection and classification of cancer in screening mammography. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VI 23 (pp 241-250). Springer International Publishing.
[http://dx.doi.org/10.1007/978-3-030-59725-2_24]
[75]
D’Amico NC, Grossi E, Valbusa G, et al. A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI. Eur Radiol Exp 2020; 4(1): 5.
[http://dx.doi.org/10.1186/s41747-019-0131-4] [PMID: 31993839]
[76]
Sayed AM, Zaghloul E, Nassef TM. Automatic classification of breast tumors using features extracted from magnetic resonance images. Proc Comput Sci 2016; 95: 392-8.
[http://dx.doi.org/10.1016/j.procs.2016.09.350]
[77]
Rasti R, Teshnehlab M, Phung SL. Breast cancer diagnosis in DCE-MRI using mixture ensemble of convolutional neural networks. Pattern Recognit 2017; 72: 381-90.
[http://dx.doi.org/10.1016/j.patcog.2017.08.004]
[78]
Herent P, Schmauch B, Jehanno P, et al. Detection and characterization of MRI breast lesions using deep learning. Diagn Interv Imaging 2019; 100(4): 219-25.
[http://dx.doi.org/10.1016/j.diii.2019.02.008] [PMID: 30926444]
[79]
Yurttakal AH, Erbay H, İkizceli T, Karaçavuş S. Detection of breast cancer via deep convolution neural networks using MRI images. Multimedia Tools Appl 2020; 79(21-22): 15555-73.
[http://dx.doi.org/10.1007/s11042-019-7479-6]
[80]
Ha R, Mutasa S, Karcich J, et al. Predicting breast cancer molecular subtype with MRI dataset utilizing convolutional neural network algorithm. J Digit Imaging 2019; 32(2): 276-82.
[http://dx.doi.org/10.1007/s10278-019-00179-2] [PMID: 30706213]
[81]
Zhou L. Transfer learning-based DCE-MRI method for identifying differentiation between benign and malignant breast tumors. IEEE Access 2020; 8: 17527-34.
[http://dx.doi.org/10.1109/ACCESS.2020.2967820]
[82]
Sheth D, Giger ML. Artificial intelligence in the interpretation of breast cancer on MRI. J Magn Reson Imaging 2020; 51(5): 1310-24.
[http://dx.doi.org/10.1002/jmri.26878] [PMID: 31343790]
[83]
Jalalian A, Mashohor S, Mahmud R, Karasfi B, Iqbal Saripan M, Ramli AR. Computer-Assisted Diagnosis System for Breast Cancer in Computed Tomography Laser Mammography (CTLM). J Digit Imaging 2017; 30(6): 796-811.
[http://dx.doi.org/10.1007/s10278-017-9958-5] [PMID: 28429195]
[84]
Retson TA, Eghtedari M. Computer-aided detection/diagnosis in breast imaging: A focus on the evolving FDA regulations for using software as a medical device. Curr Radiol Rep 2020; 8(6): 1-7.
[http://dx.doi.org/10.1007/s40134-020-00350-6]
[85]
Hamidinekoo A, Denton E, Rampun A, Honnor K, Zwiggelaar R. Deep learning in mammography and breast histology, an overview and future trends. Med Image Anal 2018; 47: 45-67.
[http://dx.doi.org/10.1016/j.media.2018.03.006] [PMID: 29679847]
[86]
Debelee TG, Schwenker F, Ibenthal A, Yohannes D. Survey of deep learning in breast cancer image analysis. Evol Syst 2020; 11(1): 143-63.
[http://dx.doi.org/10.1007/s12530-019-09297-2]
[87]
Ekici S, Jawzal H. Breast cancer diagnosis using thermography and convolutional neural networks. Med Hypotheses 2020; 137: 109542.
[http://dx.doi.org/10.1016/j.mehy.2019.109542]
[88]
Cao Z, Duan L, Yang G, Yue T, Chen Q. An experimental study on breast lesion detection and classification from ultrasound images using deep learning architectures. BMC Med Imaging 2019; 19(1): 51.
[http://dx.doi.org/10.1186/s12880-019-0349-x] [PMID: 31262255]
[89]
Yap MH, Pons G, Marti J, et al. Automated breast ultrasound lesions detection using convolutional neural networks. IEEE J Biomed Health Inform 2018; 22(4): 1218-26.
[http://dx.doi.org/10.1109/JBHI.2017.2731873] [PMID: 28796627]
[90]
Pi Y. Automated diagnosis of multi-plane breast ultrasonography images using deep neural networks. Neurocomputing 2020; 403: 371-82.
[http://dx.doi.org/10.1016/j.neucom.2020.04.123]
[91]
Ciritsis A, Rossi C, Eberhard M, Marcon M, Becker AS, Boss A. Automatic classification of ultrasound breast lesions using a deep convolutional neural network mimicking human decision-making. Eur Radiol 2019; 29(10): 5458-68.
[http://dx.doi.org/10.1007/s00330-019-06118-7] [PMID: 30927100]
[92]
Wang Y, Choi EJ, Choi Y, Zhang H, Jin GY, Ko SB. Breast cancer classification in automated breast ultrasound using multiview convolutional neural network with transfer learning. Ultrasound Med Biol 2020; 46(5): 1119-32.
[http://dx.doi.org/10.1016/j.ultrasmedbio.2020.01.001] [PMID: 32059918]
[93]
Byra M, Galperin M, Ojeda-Fournier H, et al. Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion. Med Phys 2019; 46(2): 746-55.
[http://dx.doi.org/10.1002/mp.13361] [PMID: 30589947]
[94]
Abdel-Nasser M, Melendez J, Moreno A, Omer OA, Puig D. Breast tumor classification in ultrasound images using texture analysis and super-resolution methods. Eng Appl Artif Intell 2017; 59: 84-92.
[95]
Moon WK, Lee YW, Ke HH, Lee SH, Huang CS, Chang RF. Computer-aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks. Comput Methods Programs Biomed 2020; 190: 105361.
[http://dx.doi.org/10.1016/j.cmpb.2020.105361] [PMID: 32007839]
[96]
Marcon M, Ciritsis A, Rossi C, et al. Diagnostic performance of machine learning applied to texture analysis-derived features for breast lesion characterisation at automated breast ultrasound: a pilot study. Eur Radiol Exp 2019; 3(1): 44.
[http://dx.doi.org/10.1186/s41747-019-0121-6] [PMID: 31676937]
[97]
Huang Y, Han L, Dou H, et al. Two-stage CNNs for computerized BI-RADS categorization in breast ultrasound images. Biomed Eng Online 2019; 18(1): 8.
[http://dx.doi.org/10.1186/s12938-019-0626-5] [PMID: 30678680]
[98]
Alyafeai Z, Ghouti L. A fully-automated deep learning pipeline for cervical cancer classification. Expert Syst Appl 2020; 141: 112951.
[http://dx.doi.org/10.1016/j.eswa.2019.112951]
[99]
Zhang T. Cervical precancerous lesions classification using pre-trained densely connected convolutional networks with colposcopy images. Biomed Signal Process Control 2020; 55: 101566.
[http://dx.doi.org/10.1016/j.bspc.2019.101566]
[100]
Saini SK, Bansal V, Kaur R, Juneja M. ColpoNet for automated cervical cancer screening using colposcopy images. Mach Vis Appl 2020; 31(3): 1-15.
[http://dx.doi.org/10.1007/s00138-020-01063-8]
[101]
Xue D. An application of transfer learning and ensemble learning techniques for cervical histopathology image classification. IEEE Access 2020; 8: 104603-18.
[http://dx.doi.org/10.1109/ACCESS.2020.2999816]
[102]
Li C. Cervical histopathology image classification using multilayer hidden conditional random fields and weakly supervised learning. IEEE Access 2019; 7: 90378-97.
[http://dx.doi.org/10.1109/ACCESS.2019.2924467]
[103]
Kurnianingsih , Allehaibi KH, Nugroho LE, Widyawan Lazuardi, L, Prabuwono AS, Mantoro T. Segmentation and classification of cervical cells using deep learning. IEEE Access 2019; 7: 116925-41.
[http://dx.doi.org/10.1109/ACCESS.2019.2936017]
[104]
Li C. A review for cervical histopathology image analysis using machine vision approaches. Artif Intell Rev 2020; 53(7): 4821-62.
[http://dx.doi.org/10.1007/s10462-020-09808-7]
[105]
Ghoneim A, Muhammad G, Hossain MS. Cervical cancer classification using convolutional neural networks and extreme learning machines. Future Gener Comput Syst 2020; 102: 643-9.
[http://dx.doi.org/10.1016/j.future.2019.09.015]
[106]
Zhang L. Le Lu, Nogues I, Summers RM, Liu S, Yao J. DeepPap: Deep convolutional networks for cervical cell classification. IEEE J Biomed Health Inform 2017; 21(6): 1633-43.
[http://dx.doi.org/10.1109/JBHI.2017.2705583] [PMID: 28541229]
[107]
Lequan Yu, Hao Chen, Qi Dou, Jing Qin, Heng PA. Integrating online and offline three-dimensional deep learning for automated polyp detection in colonoscopy videos. IEEE J Biomed Health Inform 2017; 21(1): 65-75.
[http://dx.doi.org/10.1109/JBHI.2016.2637004] [PMID: 28114049]
[108]
Jia X, Xing X, Yuan Y, Xing L, Meng MQH. Wireless Capsule Endoscopy: A new tool for cancer screening in the colon with deep-learning-based polyp recognition. Proc IEEE 2020; 108(1): 178-97.
[http://dx.doi.org/10.1109/JPROC.2019.2950506]
[109]
Sirinukunwattana K, Ahmed Raza SE. Yee-Wah Tsang, Snead DRJ, Cree IA, Rajpoot NM. Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans Med Imaging 2016; 35(5): 1196-206.
[http://dx.doi.org/10.1109/TMI.2016.2525803] [PMID: 26863654]
[110]
Xu Y, Jiao L, Wang S, et al. Multi-label classification for colon cancer using histopathological images. Microsc Res Tech 2013; 76(12): 1266-77.
[http://dx.doi.org/10.1002/jemt.22294] [PMID: 24123468]
[111]
Zhou, Y., Graham, S., Alemi Koohbanani, N., Shaban, M., Heng, P.A. and Rajpoot, N., 2019. Cgc-net: Cell graph convolutional network for grading of colorectal cancer histology images. In Proceedings of the IEEE/CVF international conference on computer vision workshops. (pp. 0-0).
[112]
Iqbal T, Ali H. Generative Adversarial Network for Medical Images (MI-GAN). J Med Syst 2018; 42(11): 231.
[http://dx.doi.org/10.1007/s10916-018-1072-9] [PMID: 30315368]
[113]
Mahapatra D, Bozorgtabar B, Garnavi R. Image super-resolution using progressive generative adversarial networks for medical image analysis. Comput Med Imaging Graph 2019; 71: 30-9.
[http://dx.doi.org/10.1016/j.compmedimag.2018.10.005] [PMID: 30472408]
[114]
Horie Y, Yoshio T, Aoyama K, et al. Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks. Gastrointest Endosc 2019; 89(1): 25-32.
[http://dx.doi.org/10.1016/j.gie.2018.07.037] [PMID: 30120958]
[115]
Ghatwary N, Zolgharni M, Ye X. Early esophageal adenocarcinoma detection using deep learning methods. Int J CARS 2019; 14(4): 611-21.
[http://dx.doi.org/10.1007/s11548-019-01914-4] [PMID: 30666547]
[116]
Ohmori M, Ishihara R, Aoyama K, et al. Endoscopic detection and differentiation of esophageal lesions using a deep neural network. Gastrointest Endosc 2020; 91(2): 301-309.e1.
[http://dx.doi.org/10.1016/j.gie.2019.09.034] [PMID: 31585124]
[117]
Talo M. Automated classification of histopathology images using transfer learning. Artif Intell Med 2019; 101: 101743.
[http://dx.doi.org/10.1016/j.artmed.2019.101743] [PMID: 31813483]
[118]
Tripathi S, Singh SK. Cell nuclei classification in histopathological images using hybrid OLConvNet. ACM Trans Multimed Comput Commun Appl 2020; 16(1s): 1-22.
[http://dx.doi.org/10.1145/3345318]
[119]
Kosaraju SC, Hao J, Koh HM, Kang M. Deep-Hipo: Multi-scale receptive field deep learning for histopathological image analysis. Methods 2020; 179: 3-13.
[http://dx.doi.org/10.1016/j.ymeth.2020.05.012] [PMID: 32442672]
[120]
Xu Y, Jia Z, Wang LB, et al. Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features. BMC Bioinformatics 2017; 18(1): 281.
[http://dx.doi.org/10.1186/s12859-017-1685-x] [PMID: 28549410]
[121]
Zhai J, Shen W, Singh I, Wanyama T, Gao Z. A review of the evolution of deep learning architectures and comparison of their performances for histopathologic cancer detection. Proc Manuf 2020; 46(2019): 683-9.
[http://dx.doi.org/10.1016/j.promfg.2020.03.097]
[122]
Giger ML. Machine Learning in Medical Imaging. J Am Coll Radiol 2018; 15 (3 Pt B): 512-20.
[http://dx.doi.org/10.1016/j.jacr.2017.12.028] [PMID: 29398494]
[123]
Zhai Z, Staring M, Ota H, Stoel BC. Pulmonary vessel tree matching for quantifying changes in vascular morphology. International Conference on Medical Image Computing and Computer-Assisted Intervention MICCAI 2018: Medical Image Computing and Computer Assisted Intervention – MICCAI. 2018; 11071: 517-24.
[http://dx.doi.org/10.1007/978-3-030-00934-2_58]
[124]
Kawauchi K, Furuya S, Hirata K, et al. A convolutional neural network-based system to classify patients using FDG PET/CT examinations. BMC Cancer 2020; 20(1): 227.
[http://dx.doi.org/10.1186/s12885-020-6694-x] [PMID: 32183748]
[125]
Domingues I, Pereira G, Martins P, Duarte H, Santos J, Abreu PH. Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET. Artif Intell Rev 2020; 53(6): 4093-160.
[http://dx.doi.org/10.1007/s10462-019-09788-3]
[126]
Rubin DL. Artificial intelligence in imaging: The radiologist’s role. J Am Coll Radiol 2019; 16 (9 Pt B): 1309-17.
[http://dx.doi.org/10.1016/j.jacr.2019.05.036] [PMID: 31492409]
[127]
Brink JA, Arenson RL, Grist TM, Lewin JS, Enzmann D. Bits and bytes: The future of radiology lies in informatics and information technology. Eur Radiol 2017; 27(9): 3647-51.
[http://dx.doi.org/10.1007/s00330-016-4688-5] [PMID: 28280932]
[128]
Tajbakhsh N, Shin JY, Gurudu SR, et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE Trans Med Imaging 2016; 35(5): 1299-312.
[http://dx.doi.org/10.1109/TMI.2016.2535302] [PMID: 26978662]
[129]
Yamashita R, Nishio M, Do RKG, Togashi K. Convolutional neural networks: An overview and application in radiology. Insights Imaging 2018; 9(4): 611-29.
[http://dx.doi.org/10.1007/s13244-018-0639-9] [PMID: 29934920]
[130]
Ker J, Wang L, Rao J, Lim T. Deep learning applications in medical image analysis. IEEE Access 2017; 6: 9375-9.
[http://dx.doi.org/10.1109/ACCESS.2017.2788044]
[131]
Yue L, Tian D, Chen W, Han X, Yin M. Deep learning for heterogeneous medical data analysis. World Wide Web (Bussum) 2020; 23(5): 2715-37.
[http://dx.doi.org/10.1007/s11280-019-00764-z]
[132]
Hu Z, Tang J, Wang Z, Zhang K, Zhang L, Sun Q. Deep learning for image-based cancer detection and diagnosis−A survey. Pattern Recogn 2018; 83: 134-49.
[http://dx.doi.org/10.1016/j.patcog.2018.05.014]
[133]
Fourcade A, Khonsari RH. Deep learning in medical image analysis: A third eye for doctors. J Stomatol Oral Maxillofac Surg 2019; 120(4): 279-88.
[http://dx.doi.org/10.1016/j.jormas.2019.06.002] [PMID: 31254638]
[134]
Sahiner B, Pezeshk A, Hadjiiski LM, et al. Deep learning in medical imaging and radiation therapy. Med Phys 2019; 46(1): e1-e36.
[http://dx.doi.org/10.1002/mp.13264] [PMID: 30367497]
[135]
Coccia M. Deep learning technology for improving cancer care in society: New directions in cancer imaging driven by artificial intelligence. Technol Soc 2020; 60: 101198.
[http://dx.doi.org/10.1016/j.techsoc.2019.101198]
[136]
Nichols JA, Herbert Chan HW, Baker MAB. Machine learning: applications of artificial intelligence to imaging and diagnosis. Biophys Rev 2019; 11(1): 111-8.
[http://dx.doi.org/10.1007/s12551-018-0449-9] [PMID: 30182201]
[137]
Anwar SM, Majid M, Qayyum A, Awais M, Alnowami M, Khan MK. Medical image analysis using convolutional neural networks: A review. J Med Syst 2018; 42(11): 226.
[http://dx.doi.org/10.1007/s10916-018-1088-1] [PMID: 30298337]
[138]
Zhang, Y., Wang, S., Zhao, H., Guo, Z. and Sun, D., 2021. CT image classification based on convolutional neural network. Neural Computing and Applications 33 pp. 8191-8200.
[http://dx.doi.org/10.1007/s00521-020-04933-4]
[139]
Zhou L, Zhang Z, Chen YC, Zhao ZY, Yin XD, Jiang HB. A deep learning-based radiomics model for differentiating benign and malignant renal tumors. Transl Oncol 2019; 12(2): 292-300.
[http://dx.doi.org/10.1016/j.tranon.2018.10.012] [PMID: 30448734]
[140]
Hussain, M.A., Hamarneh, G. and, Garbi, R., 2019. ImHistNet: Learnable image histogram based DNN with application to noninvasive determination of carcinoma grades in CT scans. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part VI 22 (pp 130-138) Springer International Publishing.
[http://dx.doi.org/10.1007/978-3-030-32226-7_15]
[141]
Xiong H. Computer-aided diagnosis of laryngeal cancer via deep learning based on laryngoscopic images. EBioMedicine 2019; 48: 92-9.
[142]
Das A, Acharya UR, Panda SS, Sabut S. Deep learning based liver cancer detection using watershed transform and Gaussian mixture model techniques. Cogn Syst Res 2019; 54: 165-75.
[http://dx.doi.org/10.1016/j.cogsys.2018.12.009]
[143]
Jabarulla MY, Lee HN. Computer aided diagnostic system for ultrasound liver images: A systematic review. Optik (Stuttg) 2017; 140: 1114-26.
[http://dx.doi.org/10.1016/j.ijleo.2017.05.013]
[144]
Wu H, Gao R, Sheng YP, Chen B, Li S. SDAE-GAN: Enable high-dimensional pathological images in liver cancer survival prediction with a policy gradient based data augmentation method. Med Image Anal 2020; 62: 101640.
[http://dx.doi.org/10.1016/j.media.2020.101640] [PMID: 32120270]
[145]
Sun C, Xu A, Liu D, Xiong Z, Zhao F, Ding W. Deep learning-based classification of liver cancer histopathology images using only global labels. IEEE J Biomed Health Inform 2020; 24(6): 1643-51.
[http://dx.doi.org/10.1109/JBHI.2019.2949837] [PMID: 31670686]
[146]
Hamm CA, Wang CJ, Savic LJ, et al. Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI. Eur Radiol 2019; 29(7): 3338-47.
[http://dx.doi.org/10.1007/s00330-019-06205-9] [PMID: 31016442]
[147]
Tan T, Li Z, Liu H, et al. Optimize transfer learning for lung diseases in bronchoscopy using a new concept: Sequential fine-tuning. IEEE J Transl Eng Health Med 2018; 6: 1800808.
[http://dx.doi.org/10.1109/JTEHM.2018.2865787] [PMID: 30324036]
[148]
Ozdemir O, Russell RL, Berlin AA. A 3D probabilistic deep learning system for detection and diagnosis of lung cancer using low-dose CT Scans. IEEE Trans Med Imaging 2020; 39(5): 1419-29.
[http://dx.doi.org/10.1109/TMI.2019.2947595] [PMID: 31675322]
[149]
Zhao X. A cross-modal 3D deep learning for accurate lymph node metastasis prediction in clinical stage T1 lung adenocarcinoma. Lung Cancer 2020; 145: 10-7.
[150]
Jin H, Li Z, Tong R, Lin L. A deep 3D residual CNN for false-positive reduction in pulmonary nodule detection. Med Phys 2018; 45(5): 2097-107.
[http://dx.doi.org/10.1002/mp.12846] [PMID: 29500816]
[151]
Pang S, Zhang Y, Ding M, Wang X, Xie X. A Deep Model for Lung Cancer Type Identification by Densely Connected Convolutional Networks and Adaptive Boosting. IEEE Access 2020; 8: 4799-805.
[http://dx.doi.org/10.1109/ACCESS.2019.2962862]
[152]
Cao H, Liu H, Song E, et al. A two-stage convolutional neural networks for lung nodule detection. IEEE J Biomed Health Inform 2020; 24(7): 2006-15.
[http://dx.doi.org/10.1109/JBHI.2019.2963720] [PMID: 31905154]
[153]
Zheng G, Han G, Soomro NQ. An inception module CNN classifiers fusion method on pulmonary nodule diagnosis by signs. Tsinghua Sci Technol 2020; 25(3): 368-83.
[http://dx.doi.org/10.26599/TST.2019.9010010]
[154]
Shen S, Han SX, Aberle DR, Bui AA, Hsu W. An interpretable deep hierarchical semantic convolutional neural network for lung nodule malignancy classification. Expert Syst Appl 2019; 128: 84-95.
[http://dx.doi.org/10.1016/j.eswa.2019.01.048] [PMID: 31296975]
[155]
Gong L, Jiang S, Yang Z, Zhang G, Wang L. Automated pulmonary nodule detection in CT images using 3D deep squeeze-and-excitation networks. Int J CARS 2019; 14(11): 1969-79.
[http://dx.doi.org/10.1007/s11548-019-01979-1] [PMID: 31028657]
[156]
Fu L, Ma J, Chen Y, Larsson R, Zhao J. Automatic detection of lung nodules using 3D deep convolutional neural networks. J Shanghai Jiaotong Univ 2019; 24(4): 517-23.
[http://dx.doi.org/10.1007/s12204-019-2084-4]
[157]
Zhang G, Yang Z, Gong L, Jiang S, Wang L, Zhang H. Classification of lung nodules based on CT images using squeeze-and-excitation network and aggregated residual transformations. Radiol Med (Torino) 2020; 125(4): 374-83.
[http://dx.doi.org/10.1007/s11547-019-01130-9] [PMID: 31916105]
[158]
Jung H, Kim B, Lee I, Lee J, Kang J. Classification of lung nodules in CT scans using three-dimensional deep convolutional neural networks with a checkpoint ensemble method. BMC Med Imaging 2018; 18(1): 48.
[http://dx.doi.org/10.1186/s12880-018-0286-0] [PMID: 30509191]
[159]
Lakshmi D, Thanaraj KP, Arunmozhi M. Convolutional neural network in the detection of lung carcinoma using transfer learning approach. Int J Imaging Syst Technol 2020; 30(2): 445-54.
[http://dx.doi.org/10.1002/ima.22394]
[160]
Liu Y, Hao P, Zhang P, Xu X, Wu J, Chen W. Dense convolutional binary-tree networks for lung nodule classification. IEEE Access 2018; 6: 49080-8.
[http://dx.doi.org/10.1109/ACCESS.2018.2865544]
[161]
Cho SI, Sun S, Mun JH, et al. Dermatologist-level classification of malignant lip diseases using a deep convolutional neural network. Br J Dermatol 2020; 182(6): 1388-94.
[http://dx.doi.org/10.1111/bjd.18459] [PMID: 31449661]
[162]
Toğaçar M, Ergen B, Cömert Z. Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks. Biocybern Biomed Eng 2020; 40(1): 23-39.
[http://dx.doi.org/10.1016/j.bbe.2019.11.004]
[163]
Zhang B. Ensemble learners of multiple deep cnns for pulmonary nodules classification using CT images. IEEE Access 2019; 7: 110358-71.
[http://dx.doi.org/10.1109/ACCESS.2019.2933670]
[164]
Yang, K., Liu, J., Tang, W., Zhang, H., Zhang, R., Gu, J., Zhu, R., Xiong, J., Ru, X. and Wu, J. Identification of benign and malignant pulmonary nodules on chest CT using improved 3D U-Net deep learning framework. Eur J Radiol. 2020; 129: p. 109013.
[http://dx.doi.org/10.1016/j.ejrad.2020.109013]
[165]
Chen G, Zhang J, Zhuo D, Pan Y, Pang C. Identification of pulmonary nodules via CT images with hierarchical fully convolutional networks. Med Biol Eng Comput 2019; 57(7): 1567-80.
[http://dx.doi.org/10.1007/s11517-019-01976-1] [PMID: 31025248]
[166]
Bonavita I, Rafael-Palou X, Ceresa M, Piella G, Ribas V, González Ballester MA. Integration of convolutional neural networks for pulmonary nodule malignancy assessment in a lung cancer classification pipeline. Comput Methods Programs Biomed 2020; 185: 105172.
[http://dx.doi.org/10.1016/j.cmpb.2019.105172] [PMID: 31710985]
[167]
Makaju S, Prasad PWC, Alsadoon A, Singh AK, Elchouemi A. Lung cancer detection using CT scan images. Proc Comput Sci 2018; 125: 107-14.
[http://dx.doi.org/10.1016/j.procs.2017.12.016]
[168]
Harsono IW, Liawatimena S, Cenggoro TW. Lung nodule detection and classification from Thorax CT-scan using RetinaNet with transfer learning. J King Saud Univ Comput Inf Sci 2022; 34(3): 567-77.
[http://dx.doi.org/10.1016/j.jksuci.2020.03.013]
[169]
Saba T, Sameh A, Khan F, Shad SA, Sharif M. Lung nodule detection based on ensemble of hand crafted and deep features. J Med Syst 2019; 43(12): 332.
[http://dx.doi.org/10.1007/s10916-019-1455-6] [PMID: 31705347]
[170]
Zhang Q, Wang H, Yoon SW, Won D, Srihari K. Lung nodule diagnosis on 3D computed tomography images using deep convolutional neural networks. Proc Manuf 2019; 39: 363-70.
[http://dx.doi.org/10.1016/j.promfg.2020.01.375]
[171]
Xu X, Wang C, Guo J, et al. MSCS-DeepLN: Evaluating lung nodule malignancy using multi-scale cost-sensitive neural networks. Med Image Anal 2020; 65: 101772.
[http://dx.doi.org/10.1016/j.media.2020.101772] [PMID: 32674041]
[172]
Zhang Z, Li X, You Q, Luo X. Multicontext 3D residual CNN for false positive reduction of pulmonary nodule detection. Int J Imaging Syst Technol 2019; 29(1): 42-9.
[http://dx.doi.org/10.1002/ima.22293]
[173]
Sori WJ, Feng J, Liu S. Multi-path convolutional neural network for lung cancer detection. Multidimens Syst Signal Process 2019; 30(4): 1749-68.
[http://dx.doi.org/10.1007/s11045-018-0626-9]
[174]
Liu K, Kang G. Multiview convolutional neural networks for lung nodule classification. Int J Imaging Syst Technol 2017; 27(1): 12-22.
[http://dx.doi.org/10.1002/ima.22206]
[175]
Wang Y, Zhang H, Chae KJ, Choi Y, Jin GY, Ko SB. Novel convolutional neural network architecture for improved pulmonary nodule classification on computed tomography. Multidimens Syst Signal Process 2020; 31(3): 1163-83.
[http://dx.doi.org/10.1007/s11045-020-00703-6]
[176]
Zheng J. Pulmonary nodule risk classification in adenocarcinoma from CT images using deep CNN with scale transfer module. IET Image Process 2020; 14(8): 1481-9.
[http://dx.doi.org/10.1049/iet-ipr.2019.0248]
[177]
Zhang C, Sun X, Dang K, et al. Toward an expert level of lung cancer detection and classification using a deep convolutional neural network. Oncologist 2019; 24(9): 1159-65.
[http://dx.doi.org/10.1634/theoncologist.2018-0908] [PMID: 30996009]
[178]
Chassagnon G, Vakalopoulou M, Paragios N, Revel MP. Artificial intelligence applications for thoracic imaging. Eur J Radiol 2020; 123(November): 108774.
[http://dx.doi.org/10.1016/j.ejrad.2019.108774] [PMID: 31841881]
[179]
Firmino M, Morais AH, Mendoça RM, Dantas MR, Hekis HR, Valentim R. Computer-aided detection system for lung cancer in computed tomography scans: review and future prospects. Biomed Eng Online 2014; 13(1): 41.
[http://dx.doi.org/10.1186/1475-925X-13-41] [PMID: 24713067]
[180]
Suresh S, Mohan S. ROI-based feature learning for efficient true positive prediction using convolutional neural network for lung cancer diagnosis. Neural Comput Appl 2020; 32(20): 15989-6009.
[http://dx.doi.org/10.1007/s00521-020-04787-w]
[181]
Kuang Y, Lan T, Peng X, Selasi GE, Liu Q, Zhang J. Unsupervised multi-discriminator generative adversarial network for lung nodule malignancy classification. IEEE Access 2020; 8: 77725-34.
[http://dx.doi.org/10.1109/ACCESS.2020.2987961]
[182]
Teramoto, A., Yamada, A., Kiriyama, Y., Tsukamoto, T., Yan, K., Zhang, L., Imaizumi, K., Saito, K. and Fujita, H., 2019. Automated classification of benign and malignant cells from lung cytological images using deep convolutional neural network. Informatics in Medicine Unlocked 16: p. 10.
[http://dx.doi.org/10.1016/j.imu.2019.100205]
[183]
Chen CH, Lee YW, Huang YS, et al. Computer-aided diagnosis of endobronchial ultrasound images using convolutional neural network. Comput Methods Programs Biomed 2019; 177: 175-82.
[http://dx.doi.org/10.1016/j.cmpb.2019.05.020] [PMID: 31319946]
[184]
Pham HHN, Futakuchi M, Bychkov A, Furukawa T, Kuroda K, Fukuoka J. Detection of lung cancer lymph node metastases from whole-slide histopathologic images using a two-step deep learning approach. Am J Pathol 2019; 189(12): 2428-39.
[http://dx.doi.org/10.1016/j.ajpath.2019.08.014] [PMID: 31541645]
[185]
Cui L, Li H, Hui W, et al. A deep learning-based framework for lung cancer survival analysis with biomarker interpretation. BMC Bioinformatics 2020; 21(1): 112.
[http://dx.doi.org/10.1186/s12859-020-3431-z] [PMID: 32183709]
[186]
Perez G, Arbelaez P. Automated lung cancer diagnosis using three-dimensional convolutional neural networks. Med Biol Eng Comput 2020; 58(8): 1803-15.
[http://dx.doi.org/10.1007/s11517-020-02197-7] [PMID: 32504345]
[187]
Li Y, Zhang L, Chen H, Yang N. Lung nodule detection with deep learning in 3D thoracic MR images. IEEE Access 2019; 7: 37822-32.
[http://dx.doi.org/10.1109/ACCESS.2019.2905574]
[188]
Ali N. Automatic label‐free detection of breast cancer using nonlinear multimodal imaging and the convolutional neural network ResNet50. Transl Biophoton 2019; 1(1–2)
[http://dx.doi.org/10.1002/tbio.201900003]
[189]
Shi H, Zhang ND. qiang W, Zhang YD. Multimodal lung tumor image recognition algorithm based on integrated convolutional neural network. Concurr Comput Pract Exp 2020; 32(21): 1-11.
[http://dx.doi.org/10.1002/cpe.4965]
[190]
Wang H, Zhou Z, Li Y, et al. Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images. EJNMMI Res 2017; 7(1): 11.
[http://dx.doi.org/10.1186/s13550-017-0260-9] [PMID: 28130689]
[191]
Singh GAP, Gupta PK. Performance analysis of various machine learning-based approaches for detection and classification of lung cancer in humans. Neural Comput Appl 2019; 31(10): 6863-77.
[http://dx.doi.org/10.1007/s00521-018-3518-x]
[192]
Ma J, Song Y, Tian X, Hua Y, Zhang R, Wu J. Survey on deep learning for pulmonary medical imaging. Front Med 2020; 14(4): 450-69.
[http://dx.doi.org/10.1007/s11684-019-0726-4] [PMID: 31840200]
[193]
Pesce E, Joseph Withey S, Ypsilantis PP, Bakewell R, Goh V, Montana G. Learning to detect chest radiographs containing pulmonary lesions using visual attention networks. Med Image Anal 2019; 53: 26-38.
[http://dx.doi.org/10.1016/j.media.2018.12.007] [PMID: 30660946]
[194]
Gehlot S, Gupta A, Gupta R. SDCT-AuxNetθ: DCT augmented stain deconvolutional CNN with auxiliary classifier for cancer diagnosis. Med Image Anal 2020; 61: 101661.
[http://dx.doi.org/10.1016/j.media.2020.101661] [PMID: 32066066]
[195]
Rubin M, Stein O, Turko NA, et al. TOP-GAN: Stain-free cancer cell classification using deep learning with a small training set. Med Image Anal 2019; 57: 176-85.
[http://dx.doi.org/10.1016/j.media.2019.06.014] [PMID: 31325721]
[196]
Öztürk Ş, Akdemir B. HIC-net: A deep convolutional neural network model for classification of histopathological breast images. Comput Electr Eng 2019; 76: 299-310.
[http://dx.doi.org/10.1016/j.compeleceng.2019.04.012]
[197]
Xu S. An Early Diagnosis of Oral Cancer based on Three- Dimensional Convolutional Neural Networks. IEEE Access 2019; 7: 158603-11.
[http://dx.doi.org/10.1109/ACCESS.2019.2950286]
[198]
Das N, Hussain E, Mahanta LB. Automated classification of cells into multiple classes in epithelial tissue of oral squamous cell carcinoma using transfer learning and convolutional neural network. Neural Netw 2020; 128: 47-60.
[http://dx.doi.org/10.1016/j.neunet.2020.05.003] [PMID: 32416467]
[199]
Panigrahi, S. and, Swarnkar, T., 2019, November. Automated classification of oral cancer histopathology images using convolutional neural network. In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 1232-1234). IEEE.
[http://dx.doi.org/10.1109/BIBM47256.2019.8982979]
[200]
Jeyaraj PR, Samuel Nadar ER. Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm. J Cancer Res Clin Oncol 2019; 145(4): 829-37.
[http://dx.doi.org/10.1007/s00432-018-02834-7] [PMID: 30603908]
[201]
BenTaieb A, Li-Chang H, Huntsman D, Hamarneh G. A structured latent model for ovarian carcinoma subtyping from histopathology slides. Med Image Anal 2017; 39: 194-205.
[http://dx.doi.org/10.1016/j.media.2017.04.008] [PMID: 28521242]
[202]
Liu KL, Wu T, Chen PT, et al. Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation. Lancet Digit Health 2020; 2(6): e303-13.
[http://dx.doi.org/10.1016/S2589-7500(20)30078-9] [PMID: 33328124]
[203]
Saha A, Tso S, Rabski J, Sadeghian A, Cusimano MD. Machine learning applications in imaging analysis for patients with pituitary tumors: a review of the current literature and future directions. Pituitary 2020; 23(3): 273-93.
[http://dx.doi.org/10.1007/s11102-019-01026-x] [PMID: 31907710]
[204]
Abbasi AA, Hussain L, Awan IA, et al. Detecting prostate cancer using deep learning convolution neural network with transfer learning approach. Cogn Neurodynamics 2020; 14(4): 523-33.
[http://dx.doi.org/10.1007/s11571-020-09587-5] [PMID: 32655715]
[205]
Zhu Y, Wang L, Liu M, et al. MRI-based prostate cancer detection with high-level representation and hierarchical classification. Med Phys 2017; 44(3): 1028-39.
[http://dx.doi.org/10.1002/mp.12116] [PMID: 28107548]
[206]
Abraham B, Nair MS. Computer-aided diagnosis of clinically significant prostate cancer from MRI images using sparse autoencoder and random forest classifier. Biocybern Biomed Eng 2018; 38(3): 733-44.
[http://dx.doi.org/10.1016/j.bbe.2018.06.009]
[207]
Dhengre N, Sinha S, Chinni B, Dogra V, Rao N. Computer aided detection of prostate cancer using multiwavelength photoacoustic data with convolutional neural network. Biomed Signal Process Control 2020; 60: 101952.
[http://dx.doi.org/10.1016/j.bspc.2020.101952]
[208]
Feng Y, Yang F, Zhou X, et al. A Deep Learning Approach for Targeted Contrast-Enhanced Ultrasound Based Prostate Cancer Detection. IEEE/ACM Trans Comput Biol Bioinformatics 2019; 16(6): 1794-801.
[http://dx.doi.org/10.1109/TCBB.2018.2835444] [PMID: 29993750]
[209]
Azizi S, Bayat S, Yan P, et al. Deep recurrent neural networks for prostate cancer detection: Analysis of temporal enhanced ultrasound. IEEE Trans Med Imaging 2018; 37(12): 2695-703.
[http://dx.doi.org/10.1109/TMI.2018.2849959] [PMID: 29994471]
[210]
Kwak JT, Hewitt SM. Nuclear architecture analysis of prostate cancer via convolutional neural networks. IEEE Access 2017; 5: 18526-33.
[http://dx.doi.org/10.1109/ACCESS.2017.2747838]
[211]
Yu, L., Chen, H., Dou, Q., Qin, J. and Heng, P.A., 2016. Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE transactions on medical imaging 36(4): pp. 994-1004.
[http://dx.doi.org/10.1109/TMI.2016.2642839]
[212]
Wei L, Ding K, Hu H. Automatic Skin Cancer Detection in Dermoscopy Images Based on Ensemble Lightweight Deep Learning Network. IEEE Access 2020; 8: 99633-47.
[http://dx.doi.org/10.1109/ACCESS.2020.2997710]
[213]
Han SS, Kim MS, Lim W, Park GH, Park I, Chang SE. Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm. J Invest Dermatol 2018; 138(7): 1529-38.
[http://dx.doi.org/10.1016/j.jid.2018.01.028] [PMID: 29428356]
[214]
Wahba MA, Ashour AS, Napoleon SA, Abd Elnaby MM, Guo Y. Combined empirical mode decomposition and texture features for skin lesion classification using quadratic support vector machine. Health Inf Sci Syst 2017; 5(1): 10.
[http://dx.doi.org/10.1007/s13755-017-0033-x] [PMID: 29142740]
[215]
Codella NCF. Deep learning ensembles for melanoma recognition in dermoscopy images. IBM J Res Develop 2017; 61(4): 1-15.
[http://dx.doi.org/10.1147/JRD.2017.2708299]
[216]
Sultana NN, Mandal B, Puhan NB. Deep residual network with regularised fisher framework for detection of melanoma. IET Comput Vis 2018; 12(8): 1096-104.
[http://dx.doi.org/10.1049/iet-cvi.2018.5238]
[217]
Fink C, Blum A, Buhl T, et al. Diagnostic performance of a deep learning convolutional neural network in the differentiation of combined naevi and melanomas. J Eur Acad Dermatol Venereol 2020; 34(6): 1355-61.
[http://dx.doi.org/10.1111/jdv.16165] [PMID: 31856342]
[218]
Mahbod A, Schaefer G, Ellinger I, Ecker R, Pitiot A, Wang C. Fusing fine-tuned deep features for skin lesion classification. Comput Med Imaging Graph 2019; 71: 19-29.
[http://dx.doi.org/10.1016/j.compmedimag.2018.10.007] [PMID: 30458354]
[219]
Serte S, Demirel H. Gabor wavelet-based deep learning for skin lesion classification. Comput Biol Med 2019; 113: 103423.
[http://dx.doi.org/10.1016/j.compbiomed.2019.103423] [PMID: 31499395]
[220]
Guo S, Yang Z. Multi-Channel-ResNet: An integration framework towards skin lesion analysis. Informatics Med Unlocked 2018; 12: 67-74.
[http://dx.doi.org/10.1016/j.imu.2018.06.006]
[221]
Hameed, N., Shabut, A.M.M, Ghosh, M.K. and Hossain, M.A., 2020. Multi-class multi-level classification algorithm for skin lesions classification using machine learning techniques. Expert Systems with Applications 141: p. 112961.
[http://dx.doi.org/10.1016/j.eswa.2019.112961]
[222]
Al-Masni MA, Kim DH, Kim TS. Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification. Comput Methods Programs Biomed 2020; 190: 105351.
[http://dx.doi.org/10.1016/j.cmpb.2020.105351] [PMID: 32028084]
[223]
Albahar MA. Skin lesion classification using convolutional neural network with novel regularizer. IEEE Access 2019; 7: 38306-13.
[http://dx.doi.org/10.1109/ACCESS.2019.2906241]
[224]
Hosny KM, Kassem MA, Foaud MM. Skin melanoma classification using ROI and data augmentation with deep convolutional neural networks. Multimedia Tools Appl 2020; 79(33–34): 24029-55.
[http://dx.doi.org/10.1007/s11042-020-09067-2]
[225]
Zhao, X.Y.Y, Wu, X., Li, F.F., Li, Y., Huang, W.H., Huang, K., He, X.Y., Fan, W., Wu, Z., Chen, M.L. and Li, J., 2019. The application of deep learning in the risk grading of skin tumors for patients using clinical images. J Med Syst 43 pp. 1-7.
[http://dx.doi.org/10.1007/s10916-019-1414-2]
[226]
Mahbod A, Schaefer G, Wang C, Dorffner G, Ecker R, Ellinger I. Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification. Comput Methods Programs Biomed 2020; 193: 105475.
[http://dx.doi.org/10.1016/j.cmpb.2020.105475] [PMID: 32268255]
[227]
Hosseinzadeh Kassani S, Hosseinzadeh Kassani P. A comparative study of deep learning architectures on melanoma detection. Tissue Cell 2019; 58: 76-83.
[http://dx.doi.org/10.1016/j.tice.2019.04.009] [PMID: 31133249]
[228]
Okur E, Turkan M. A survey on automated melanoma detection. Eng Appl Artif Intell 2018; 73: 50-67.
[http://dx.doi.org/10.1016/j.engappai.2018.04.028]
[229]
Marchetti MA, Liopyris K, Dusza SW, et al. Computer algorithms show potential for improving dermatologists’ accuracy to diagnose cutaneous melanoma: Results of the International Skin Imaging Collaboration 2017. J Am Acad Dermatol 2020; 82(3): 622-7.
[http://dx.doi.org/10.1016/j.jaad.2019.07.016] [PMID: 31306724]
[230]
Adegun A, Viriri S. Deep learning techniques for skin lesion analysis and melanoma cancer detection: A survey of state-of-the-art. Artif Intell Rev 2021; 54: 811-41.
[http://dx.doi.org/10.1007/s10462-020-09865-y]
[231]
Naeem A, Farooq MS, Khelifi A, Abid A. Malignant melanoma classification using deep learning: Datasets, performance measurements, challenges and opportunities. IEEE Access 2020; 8: 110575-97.
[http://dx.doi.org/10.1109/ACCESS.2020.3001507]
[232]
Hekler A, Utikal JS, Enk AH, et al. Superior skin cancer classification by the combination of human and artificial intelligence. Eur J Cancer 2019; 120: 114-21.
[http://dx.doi.org/10.1016/j.ejca.2019.07.019] [PMID: 31518967]
[233]
Magalhaes C, Mendes J, Vardasca R. The role of AI classifiers in skin cancer images. Skin Res Technol 2019; 25(5): 750-7.
[http://dx.doi.org/10.1111/srt.12713] [PMID: 31106913]
[234]
Qin Z, Liu Z, Zhu P, Xue Y. A GAN-based image synthesis method for skin lesion classification. Comput Methods Programs Biomed 2020; 195: 105568.
[http://dx.doi.org/10.1016/j.cmpb.2020.105568] [PMID: 32526536]
[235]
Gu Y, Ge Z, Bonnington CP, Zhou J. Progressive transfer learning and adversarial domain adaptation for cross-domain skin disease classification. IEEE J Biomed Health Inform 2020; 24(5): 1379-93.
[http://dx.doi.org/10.1109/JBHI.2019.2942429] [PMID: 31545748]
[236]
Akram, T., Lodhi, H.M.J., Naqvi, S.R., Naeem, S., Alhaisoni, M., Ali, M., Haider, S.A. and Qadri, N.N., 2020. A multilevel features selection framework for skin lesion classification. Hum Centric Comput Inform Sci 10: pp. 1-26.
[http://dx.doi.org/10.1186/s13673-020-00216-y]
[237]
Khan MA, Akram T, Sharif M, Javed K, Rashid M, Bukhari SAC. An integrated framework of skin lesion detection and recognition through saliency method and optimal deep neural network features selection. Neural Comput Appl 2020; 32(20): 15929-48.
[http://dx.doi.org/10.1007/s00521-019-04514-0]
[238]
Hirasawa T, Aoyama K, Tanimoto T, et al. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer 2018; 21(4): 653-60.
[http://dx.doi.org/10.1007/s10120-018-0793-2] [PMID: 29335825]
[239]
Li L, Chen Y, Shen Z, et al. Convolutional neural network for the diagnosis of early gastric cancer based on magnifying narrow band imaging. Gastric Cancer 2020; 23(1): 126-32.
[http://dx.doi.org/10.1007/s10120-019-00992-2] [PMID: 31332619]
[240]
Sun M, Liang K, Zhang W, Chang Q, Zhou X. Non-Local attention and densely-connected convolutional neural networks for malignancy suspiciousness classification of gastric ulcer. IEEE Access 2020; 8: 15812-22.
[http://dx.doi.org/10.1109/aCCESS.2020.2967350]
[241]
Luo H, Xu G, Li C, et al. Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: A multicentre, case-control, diagnostic study. Lancet Oncol 2019; 20(12): 1645-54.
[http://dx.doi.org/10.1016/S1470-2045(19)30637-0] [PMID: 31591062]
[242]
Kanayama, T., Kurose, Y., Tanaka, K., Aida, K., Satoh, S.I., Kitsuregawa, M. and Harada, T., 2019. Gastric cancer detection from endoscopic images using synthesis by GAN. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V 22 (pp 530-538) Springer International Publishing.
[http://dx.doi.org/10.1007/978-3-030-32254-0_59]
[243]
Jin P, Ji X, Kang W, et al. Artificial intelligence in gastric cancer: a systematic review. J Cancer Res Clin Oncol 2020; 146(9): 2339-50.
[http://dx.doi.org/10.1007/s00432-020-03304-9] [PMID: 32613386]
[244]
Sharma P, Patel K, Kuvera S, Dankhara F. Generative adversarial network (GANS) based training set enhancement for stomach adenocarcinoma computed tomography (CT) scan. Proc Comput Sci 2019; 160: 377-84.
[http://dx.doi.org/10.1016/j.procs.2019.11.077]
[245]
Ma J, Wu F, Zhu J, Xu D, Kong D. A pre-trained convolutional neural network based method for thyroid nodule diagnosis. Ultrasonics 2017; 73: 221-30.
[http://dx.doi.org/10.1016/j.ultras.2016.09.011] [PMID: 27668999]
[246]
Wang L, Zhang L, Zhu M, Qi X, Yi Z. Automatic diagnosis for thyroid nodules in ultrasound images by deep neural networks. Med Image Anal 2020; 61: 101665.
[http://dx.doi.org/10.1016/j.media.2020.101665] [PMID: 32062156]
[247]
Nugroho H A, Zulfanahri E L. Computer aided diagnosis for thyroid cancer system based on internal and external characteristics. J King Saud Univ 2021; 33(3): 329-39.
[http://dx.doi.org/10.1016/j.jksuci.2019.01.007]
[248]
Qin P, Wu K, Hu Y, Zeng J, Chai X. Diagnosis of benign and malignant thyroid nodules using combined conventional ultrasound and ultrasound elasticity imaging. IEEE J Biomed Health Inform 2020; 24(4): 1028-36.
[http://dx.doi.org/10.1109/JBHI.2019.2950994] [PMID: 31689223]
[249]
Acharya UR, Vinitha Sree S, Krishnan MM, Molinari F, Garberoglio R, Suri JS. Non-invasive automated 3D thyroid lesion classification in ultrasound: A class of ThyroScan™ systems. Ultrasonics 2012; 52(4): 508-20.
[http://dx.doi.org/10.1016/j.ultras.2011.11.003] [PMID: 22154208]
[250]
Liu C, Xie L, Kong W, et al. Prediction of suspicious thyroid nodule using artificial neural network based on radiofrequency ultrasound and conventional ultrasound: A preliminary study. Ultrasonics 2019; 99: 105951.
[http://dx.doi.org/10.1016/j.ultras.2019.105951] [PMID: 31323562]
[251]
Moussa O, Khachnaoui H, Guetari R, Khlifa N. Thyroid nodules classification and diagnosis in ultrasound images using fine-tuning deep convolutional neural network. Int J Imaging Syst Technol 2020; 30(1): 185-95.
[http://dx.doi.org/10.1002/ima.22363]
[252]
Chi J, Walia E, Babyn P, Wang J, Groot G, Eramian M. Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network. J Digit Imaging 2017; 30(4): 477-86.
[http://dx.doi.org/10.1007/s10278-017-9997-y] [PMID: 28695342]
[253]
Shi G, Wang J, Qiang Y, et al. Knowledge-guided synthetic medical image adversarial augmentation for ultrasonography thyroid nodule classification. Comput Methods Programs Biomed 2020; 196: 105611.
[http://dx.doi.org/10.1016/j.cmpb.2020.105611] [PMID: 32650266]
[254]
Nakagawa M, Nakaura T, Namimoto T, et al. Machine learning to differentiate T2-weighted hyperintense uterine leiomyomas from uterine sarcomas by utilizing multiparametric magnetic resonance quantitative imaging features. Acad Radiol 2019; 26(10): 1390-9.
[http://dx.doi.org/10.1016/j.acra.2018.11.014] [PMID: 30661978]
[255]
Yang K, Liu J, Tang W, et al. Identification of benign and malignant pulmonary nodules on chest CT using improved 3D U-Net deep learning framework. Eur J Radiol 2020; 129: 109013.
[http://dx.doi.org/10.1016/j.ejrad.2020.109013] [PMID: 32505895]
[256]
Song W, Li S, Liu J, et al. Multitask cascade convolution neural networks for automatic thyroid nodule detection and recognition. IEEE J Biomed Health Inform 2019; 23(3): 1215-24.
[http://dx.doi.org/10.1109/JBHI.2018.2852718] [PMID: 29994412]
[257]
Liu L, Dou Q, Chen H, Qin J, Heng PA. Multi-task deep model with margin ranking loss for lung nodule analysis. IEEE Trans Med Imaging 2020; 39(3): 718-28.
[http://dx.doi.org/10.1109/TMI.2019.2934577] [PMID: 31403410]
[258]
Li Y, Xie X, Shen L, Liu S. Reverse active learning based atrous DenseNet for pathological image classification. BMC Bioinformatics 2019; 20(1): 445.
[http://dx.doi.org/10.1186/s12859-019-2979-y] [PMID: 31455228]
[259]
Gao F, Wu T, Li J, et al. SD-CNN: A shallow-deep CNN for improved breast cancer diagnosis. Comput Med Imaging Graph 2018; 70: 53-62.
[http://dx.doi.org/10.1016/j.compmedimag.2018.09.004] [PMID: 30292910]
[260]
Martel, A.L., Abolmaesumi, P., Stoyanov, D., Mateus, D., Zuluaga, M.A., Zhou, S.K., Racoceanu, D. and Joskowicz, L. eds., 2020. Medical Image Computing and Computer Assisted Intervention– MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I (Vol. 12261). Springer Nature.
[261]
Azizi, S., Imani, F., Zhuang, B., Tahmasebi, A., Kwak, J.T., Xu, S., Uniyal, N., Turkbey, B., Choyke, P., Pinto, P. and Wood, B., 2015. Ultrasound-based detection of prostate cancer using automatic feature selection with deep belief networks. In Medical Image Computing and Computer-Assisted Intervention--MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part II 18 (pp. 70-77). Springer International Publishing
[http://dx.doi.org/10.1007/978-3-319-24571-3_9]
[262]
Amin J, Sharif M, Gul N, et al. Brain Tumor Detection by Using Stacked Autoencoders in Deep Learning. J Med Syst 2019; 44(2): 32.
[http://dx.doi.org/10.1007/s10916-019-1483-2] [PMID: 31848728]
[263]
Yan R. Breast cancer histopathological image classification using a hybrid deep neural network. Methods 2020; 173: 52-60.
[http://dx.doi.org/10.1016/j.ymeth.2019.06.014]
[264]
Saha M, Chakraborty C. Her2Net: A deep framework for semantic segmentation and classification of cell membranes and nuclei in breast cancer evaluation. IEEE Trans Image Process 2018; 27(5): 2189-200.
[http://dx.doi.org/10.1109/TIP.2018.2795742] [PMID: 29432100]
[265]
Allehaibi, K.H.S., Nugroho, L.E., Lazuardi, L., Prabuwono, A.S. and Mantoro, T., 2019. Segmentation and classification of cervical cells using deep learning. IEEE Access 7 pp. 116925-116941.
[http://dx.doi.org/10.1109/ACCESS.2019.2936017]
[266]
Bargsten L, Schlaefer A. SpeckleGAN: a generative adversarial network with an adaptive speckle layer to augment limited training data for ultrasound image processing. Int J CARS 2020; 15(9): 1427-36.
[http://dx.doi.org/10.1007/s11548-020-02203-1] [PMID: 32556953]
[267]
Xie H, Yang D, Sun N, Chen Z, Zhang Y. Automated pulmonary nodule detection in CT images using deep convolutional neural networks. Pattern Recog 2019; 85: 109-19.
[http://dx.doi.org/10.1016/j.patcog.2018.07.031]
[268]
Kong B, Sun S, Wang X, Song Q, and Zhang S. 2018, September. Invasive cancer detection utilizing compressed convolutional neural network and transfer learning. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part II (pp. 156-164). Cham: Springer International Publishing.
[http://dx.doi.org/10.1007/978-3-030-00934-2_18]
[269]
Ma X. Understanding adversarial attacks on deep learning based medical image analysis systems. Pattern Recogn 2021; 110: 107332.
[http://dx.doi.org/10.1016/j.patcog.2020.107332]
[270]
Muramatsu C. Improving breast mass classification by shared data with domain transformation using a generative adversarial network. Comput Biol Med 2020; 119: 103698.
[http://dx.doi.org/10.1016/j.compbiomed.2020.103698]
[271]
Yi X, Walia E, Babyn P. Generative adversarial network in medical imaging: A review. Med Image Anal 2019; 58: 101552.
[http://dx.doi.org/10.1016/j.media.2019.101552] [PMID: 31521965]

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