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Review Article

人工智能在癌症早期检测中的最新应用

卷 29, 期 25, 2022

发表于: 01 April, 2022

页: [4410 - 4435] 页: 26

弟呕挨: 10.2174/0929867329666220222154733

价格: $65

摘要

癌症是一种致命的疾病，通常是由各种基因突变和病理改变的积累引起的。只有在早期阶段发现，才能降低死亡率，因为在许多身体区域的肿瘤没有转移时，进行癌症治疗更有效。然而，癌症的早期检测充满了困难。人工智能（AI）的进步为有效和早期检测这种致命疾病开辟了新的领域。人工智能算法具有出色的能力，能够很好地处理呈现或输入系统的各种任务。大量的研究已经产生了机器学习和深度学习辅助的癌症预测模型，从以前可访问的数据中检测癌症，具有更好的准确性、敏感性和特异性。据观察，通过实施高效的图像处理技术和数据分割增强方法以及先进的算法，预测模型在将馈送数据分类为良性、恶性或正常数据方面的准确性得到了提高。在这篇综述中，我们分析了最近用于诊断最常见的乳腺癌、肺癌、脑癌和皮肤癌的基于人工智能的模型。现有的AI技术、数据准备、建模过程和性能评估都包括在审查中。

关键词: 人工智能、算法、癌症、深度学习、诊断、机器学习。

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[1]
Hamada, G.; Rida, A. Orthopaedics and orthopaedic diseases in ancient and modern Egypt. Clin. Orthop. Relat. Res., 1972, 89(89), 253-268.
[PMID: 4566947]
[2]
Haas, L.F. Papyrus of Ebers and Smith. J. Neurol. Neurosurg. Psychiatry, 1999, 67(5), 578.
[http://dx.doi.org/10.1136/jnnp.67.5.578] [PMID: 10519860]
[3]
Siegel, R.L.; Miller, K.D.; Fuchs, H.E.; Jemal, A. Cancer statistics, 2021. CA Cancer J. Clin., 2021, 71(1), 7-33.
[http://dx.doi.org/10.3322/caac.21654] [PMID: 33433946]
[4]
Fouad, Y.A.; Aanei, C. Revisiting the hallmarks of cancer. Am. J. Cancer Res., 2017, 7(5), 1016-1036.
[PMID: 28560055]
[5]
Santos, M.K.; Ferreira Júnior, J.R.; Wada, D.T.; Tenório, A.P.M.; Barbosa, M.H.N.; Marques, P.M.A. Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: Advances in imaging towards to precision medicine. Radiol. Bras., 2019, 52(6), 387-396.
[http://dx.doi.org/10.1590/0100-3984.2019.0049] [PMID: 32047333]
[6]
Bini, S.A. Artificial intelligence, machine learning, deep learning, and cognitive computing: What do these terms mean and how will they impact health care? J. Arthroplasty, 2018, 33(8), 2358-2361.
[http://dx.doi.org/10.1016/j.arth.2018.02.067] [PMID: 29656964]
[7]
Maxmen, J.S. The post-physician era: medicine in the twenty-first century; Wiley: Hoboken, 1976.
[8]
Naylor, C.D. On the prospects for a (Deep) learning health care system. JAMA, 2018, 320(11), 1099-1100.
[http://dx.doi.org/10.1001/jama.2018.11103] [PMID: 30178068]
[9]
Topol, E.J. High-performance medicine: The convergence of human and artificial intelligence. Nat. Med., 2019, 25(1), 44-56.
[http://dx.doi.org/10.1038/s41591-018-0300-7] [PMID: 30617339]
[10]
US Food and Drug Administration. Computer-assisted detection devices applied to radiology images and radiology device data—Premarket notification [510 (k)] submissions. 2012. Avaialble from: https://www.fda.gov/media/77635/download (Accessed on: June 25, 2021).
[11]
Donahue, J.; Jia, Y.; Vinyals, O.; Hoffman, J.; Zhang, N.; Tzeng, E.; Darrell, T. Decaf: a deep convolutional activation feature for generic visual recognition. ICML’14: Proceedings of the 31st International Conference on International Conference on Machine Learning, 2014 June 21 - 26 , Beijing, China 2014, pp. 647-655.
[12]
Haeberle, H.S.; Helm, J.M.; Navarro, S.M.; Karnuta, J.M.; Schaffer, J.L.; Callaghan, J.J.; Mont, M.A.; Kamath, A.F.; Krebs, V.E.; Ramkumar, P.N. Artificial intelligence and machine learning in lower extremity arthroplasty: A review. J. Arthroplasty, 2019, 34(10), 2201-2203.
[http://dx.doi.org/10.1016/j.arth.2019.05.055] [PMID: 31253449]
[13]
Kumar, R.; Sharma, A.; Siddiqui, M.H.; Tiwari, R.K. Prediction of human intestinal absorption of compounds using artificial intelligence techniques. Curr. Drug Discov. Technol., 2017, 14(4), 244-254.
[http://dx.doi.org/10.2174/1570163814666170404160911] [PMID: 28382857]
[14]
Kumar, R.; Sharma, A.; Siddiqui, M.H.; Tiwari, R.K. Prediction of drug-plasma protein binding using artificial intelligence based algorithms. Comb. Chem. High Throughput Screen., 2018, 21(1), 57-64.
[http://dx.doi.org/10.2174/1386207321666171218121557] [PMID: 29256344]
[15]
Kumar, R.; Sharma, A.; Siddiqui, M.H.; Tiwari, R.K. Prediction of metabolism of drugs using artificial intelligence: How far have we reached? Curr. Drug Metab., 2016, 17(2), 129-141.
[http://dx.doi.org/10.2174/1389200216666151103121352] [PMID: 26526829]
[16]
Kumar, R.; Sharma, A.; Siddiqui, M.H.; Tiwari, R.K. Promises of machine learning approaches in prediction of absorption of compounds. Mini Rev. Med. Chem., 2018, 18(3), 196-207.
[http://dx.doi.org/10.2174/1389557517666170315150116] [PMID: 28302041]
[17]
Saxena, D.; Sharma, A.; Siddiqui, M.H.; Kumar, R. Blood brain barrier permeability prediction using machine learning techniques: An update. Curr. Pharm. Biotechnol., 2019, 20(14), 1163-1171.
[http://dx.doi.org/10.2174/1389201020666190821145346] [PMID: 31433750]
[18]
Kumar, R.; Sharma, A.; Varadwaj, P.; Ahmad, A.; Ashraf, G.M. Classification of oral bioavailability of drugs by machine learning approaches: A comparative study. J. Comp. Int. Sci., 2011, 2, 1-18.
[http://dx.doi.org/10.6062/jcis.2011.02.03.0045]
[19]
Kumar, R.; Khan, F.U.; Sharma, A.; Aziz, I.B.; Poddar, N.K. Recent applications of artificial intelligence in detection of gastrointestinal, hepatic and pancreatic diseases. Curr. Med. Chem., 2022, 29(1), 66-85.
[http://dx.doi.org/10.2174/0929867328666210405114938] [PMID: 33820515]
[20]
Imran, A.A.Z.; Terzopoulos, D. Semi-supervised Multi-task Learning with Chest X-Ray Images. In: Machine Learning in Medical Imaging; Suk, H.I.; Liu, M.; Yan, P.; Lian, C., Eds.; Lecture Notes in Computer Science Springer: Cham, 2019; p. 11861.
[http://dx.doi.org/10.1007/978-3-030-32692-0_18]
[21]
Esteva, A.; Kuprel, B.; Novoa, R.A.; Ko, J.; Swetter, S.M.; Blau, H.M.; Thrun, S. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 2017, 542(7639), 115-118.
[http://dx.doi.org/10.1038/nature21056] [PMID: 28117445]
[22]
Albarqouni, S.; Baur, C.; Achilles, F.; Belagiannis, V.; Demirci, S.; Navab, N. AggNet: Deep learning from crowds for mitosis detection in breast cancer histology images. IEEE Trans. Med. Imaging, 2016, 35(5), 1313-1321.
[http://dx.doi.org/10.1109/TMI.2016.2528120] [PMID: 26891484]
[23]
Alipanahi, B.; Delong, A.; Weirauch, M.T.; Frey, B.J. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat. Biotechnol., 2015, 33(8), 831-838.
[http://dx.doi.org/10.1038/nbt.3300] [PMID: 26213851]
[24]
Yuan, Y.; Shi, Y.; Li, C.; Kim, J.; Cai, W.; Han, Z.; Feng, D.D. DeepGene: an advanced cancer type classifier based on deep learning and somatic point mutations. BMC Bioinformatics, 2016, 17(Suppl. 17), 476.
[http://dx.doi.org/10.1186/s12859-016-1334-9] [PMID: 28155641]
[25]
Ramkumar, P.N.; Karnuta, J.M.; Navarro, S.M.; Haeberle, H.S.; Iorio, R.; Mont, M.A.; Patterson, B.M.; Krebs, V.E. Preoperative prediction of value metrics and a patient-specific payment model for primary total hip arthroplasty: Development and validation of a deep learning model. J. Arthroplasty, 2019, 34(10), 2228-2234.e1.
[http://dx.doi.org/10.1016/j.arth.2019.04.055] [PMID: 31122849]
[26]
Sharma, A.; Kumar, R.; Semwal, R.; Aier, I.; Tyagi, P.; Varadwaj, P. DeepOlf: Deep neural network based architecture for predicting odorants and their interacting Olfactory Receptors. Trans. Comput. Biol. Bioinform., 2020, 2020, 1.
[http://dx.doi.org/10.1109/TCBB.2020.3002154]
[27]
Sharma, A.; Kumar, R.; Ranjta, S.; Varadwaj, P.K. SMILES to smell: Decoding the structure-odor relationship of chemical compounds using the deep neural network approach. J. Chem. Inf. Model., 2021, 61(2), 676-688.
[http://dx.doi.org/10.1021/acs.jcim.0c01288] [PMID: 33449694]
[28]
Aruna, S.; Rajagopalan, S.P.; Nandakishore, L.V. Knowledge based analysis of various statistical tools in detecting breast cancer. In: Proceedings of the First International Conference on Computer Science, Engineering and Applications (CCSEA 2011), Chennai, India, pp. 37-45. 2011 July 17,
[29]
Chaurasia, V.; Pal, S. Data mining techniques: to predict and resolve breast cancer survivability. IJCSMC, 2014, 3(1), 10-22.
[30]
Asri, H.; Mousannif, H.; Moatassime, H.A.; Noel, T. Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Comput. Sci., 2016, 83, 1064-1069.
[http://dx.doi.org/10.1016/j.procs.2016.04.224]
[31]
Akselrod-Ballin, A.; Chorev, M.; Shoshan, Y.; Spiro, A.; Hazan, A.; Melamed, R.; Barkan, E.; Herzel, E.; Naor, S.; Karavani, E.; Koren, G.; Goldschmidt, Y.; Shalev, V.; Rosen-Zvi, M.; Guindy, M. Predicting breast cancer by applying deep learning to linked health records and mammograms. Radiology, 2019, 292(2), 331-342.
[http://dx.doi.org/10.1148/radiol.2019182622] [PMID: 31210611]
[32]
Dhungel, N.; Carneiro, G.; Bradley, A.P. A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Med. Image Anal., 2017, 37, 114-128.
[http://dx.doi.org/10.1016/j.media.2017.01.009] [PMID: 28171807]
[33]
Moreira, I.C.; Amaral, I.; Domingues, I.; Cardoso, A.; Cardoso, M.J.; Cardoso, J.S. INbreast: toward a full-field digital mammographic database. Acad. Radiol., 2012, 19(2), 236-248.
[http://dx.doi.org/10.1016/j.acra.2011.09.014] [PMID: 22078258]
[34]
Teuwen J.; van de Leemput, S.; Gubern-Mérida, A.; Rodriguez-Ruiz, A.;,Mann, R.; Bejnordi, B. Soft Tissue Lesion Detection in Mammography Using Deep Neural Networks for Object Detection. In: MIDL'18: Proceedings of the 1st Conference on Medical Imaging with Deep Learning, Amsterdam, The Netherlands. 2018; pp. 1–9.
[35]
Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; Karpathy, A.; Khosla, A.; Bernstein, M.; Berg, A.C.; Fei-Fei, L. ImageNet large scale visual recognition challenge. Int. J. Comput. Vis., 2015, 115, 211-252.
[http://dx.doi.org/10.1007/s11263-015-0816-y]
[36]
Wang, J.; Yang, X.; Cai, H.; Tan, W.; Jin, C.; Li, L. Discrimination of breast cancer with microcalcifications on mammography by deep learning. Sci. Rep., 2016, 6, 27327.
[http://dx.doi.org/10.1038/srep27327]
[37]
Shin, H.C.; Roth, H.R.; Gao, M.; Lu, L.; Xu, Z.; Nogues, I.; Yao, J.; Mollura, D.; Summers, R.M. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imag., 2016, 35(5), 1285-1298.
[http://dx.doi.org/10.1109/TMI.2016.2528162] [PMID: 26886976]
[38]
Wu, N.; Phang, J.; Park, J.; Shen, Y.; Huang, Z.; Zorin, M.; Jastrzebski, S.; Fevry, T.; Katsnelson, J.; Kim, E.; Wolfson, S.; Parikh, U.; Gaddam, S.; Lin, L.L.Y.; Ho, K.; Weinstein, J.D.; Reig, B.; Gao, Y.; Toth, H.; Pysarenko, K.; Lewin, A.; Lee, J.; Airola, K.; Mema, E.; Chung, S.; Hwang, E.; Samreen, N.; Kim, S.G.; Heacock, L.; Moy, L.; Cho, K.; Geras, K.J. Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Trans. Med. Imaging, 2020, 39(4), 1184-1194.
[http://dx.doi.org/10.1109/TMI.2019.2945514] [PMID: 31603772]
[39]
Vaka, A.R.; Soni, B.; Reddy, S. Breast cancer detection by leveraging Machine Learning. ICT Express, 2020, 6(4), 320-324.
[http://dx.doi.org/10.1016/j.icte.2020.04.009]
[40]
Alanazi, S.A.; Kamruzzaman, M.M.; Islam Sarker, M.N.; Alruwaili, M.; Alhwaiti, Y.; Alshammari, N.; Siddiqi, M.H. Boosting breast cancer detection using convolutional neural network. J. Healthc. Eng., 2021, 2021, 5528622.
[http://dx.doi.org/10.1155/2021/5528622] [PMID: 33884157]
[41]
Khamparia, A.; Bharati, S.; Podder, P.; Gupta, D.; Khanna, A.; Phung, T.K.; Thanh, D.N.H. Diagnosis of breast cancer based on modern mammography using hybrid transfer learning. Multidimens. Syst. Signal Process., 2021, 2021, 1-19.
[http://dx.doi.org/10.1007/s11045-020-00756-7] [PMID: 33456204]
[42]
Frazer, H.M.; Qin, A.K.; Pan, H.; Brotchie, P. Evaluation of deep learning-based artificial intelligence techniques for breast cancer detection on mammograms: Results from a retrospective study using a Breast Screen Victoria dataset. J. Med. Imaging Radiat. Oncol., 2021, 65(5), 529-537.
[http://dx.doi.org/10.1111/1754-9485.13278] [PMID: 34212526]
[43]
Shen, L.; Margolies, L.R.; Rothstein, J.H.; Fluder, E.; McBride, R.; Sieh, W. Deep learning to improve breast cancer detection on screening mammography. Sci. Rep., 2019, 9(1), 12495.
[http://dx.doi.org/10.1038/s41598-019-48995-4] [PMID: 31467326]
[44]
Shen, Y.; Wu, N.; Phang, J.; Park, J.; Liu, K.; Tyagi, S.; Heacock, L.; Kim, S.G.; Moy, L.; Cho, K.; Geras, K.J. An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization. Med. Image Anal., 2021, 68, 101908.
[http://dx.doi.org/10.1016/j.media.2020.101908] [PMID: 33383334]
[45]
Liang, G.; Zheng, L. A transfer learning method with deep residual network for pediatric pneumonia diagnosis. Comput. Methods Programs Biomed., 2020, 187, 104964.
[http://dx.doi.org/10.1016/j.cmpb.2019.06.023] [PMID: 31262537]
[46]
Patra, R. Prediction of lung cancer using machine learning classifier. In: Communications in Computer and Information Science; Springer: Singapore, 2020.
[http://dx.doi.org/10.1007/978-981-15-6648-6_11]
[47]
de Carvalho Filho, A.O.; Silva, A.C.; de Paiva, A.C.; Nunes, R.A.; Gattass, M. Lung-nodule classification based on computed tomography using taxonomic diversity indexes and an SVM. J. Sign. Process Syst., 2017, 87, 179-196.
[http://dx.doi.org/10.1007/s11265-016-1134-5]
[48]
Shanthi, S.; Rajkumar, N. Lung cancer prediction using stochastic diffusion search (SDS) based feature selection and machine learning methods. Neural Process. Lett., 2021, 53, 2617-2630.
[http://dx.doi.org/10.1007/s11063-020-10192-0]
[49]
Kohad, R.; Ahire, V. Application of machine learning techniques for the diagnosis of lung cancer with ANT colony optimization. Int. J. Comput. Appl., 2015, 113(18), 34-41.
[50]
Nadkarni, N.S.; Borkar, S. Detection of lung cancer in CT Images using image processing. In: Proceedings of the 3rd International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, 2019, pp. 863-866.
[http://dx.doi.org/10.1109/ICOEI.2019.8862577]
[51]
Vas, M.; Dessai, A. Lung cancer detection system using lung CT image processing. In: Proceedings of the International Conference on Computing, Communication, Control and Automation (ICCUBEA), Pune, India, 2017, pp. 1-5. 17-18 Aug.
[http://dx.doi.org/10.1109/ICCUBEA.2017.8463851]
[52]
Nasser, I.M.; Abu-Naser, S.S. Lung cancer detection using artificial neural network. Int. J. Eng. Inform. Sys., 2019, 3(3), 17-23.
[53]
Reddy, D.; Kumar, E.N.H.; Reddy, D.; Monika, P. Integrated machine learning model for prediction of lung cancer stages from textual data using ensemble method. In: Proceedings of the 1st International Conference on Advances in Information Technology (ICAIT), Chikmagalur, India, 2019, 25-27 July.
[http://dx.doi.org/10.1109/ICAIT47043.2019.8987295]
[54]
Shen, S.; Fan, Z.; Guo, Q. Design and application of tumor prediction model based on statistical method. Comput. Assist. Surg., 2017, 22(sup1), 232-239.
[http://dx.doi.org/10.1080/24699322.2017.1389401]
[55]
Schwyzer, M.; Ferraro, D.A.; Muehlematter, U.J.; Curioni-Fontecedro, A.; Huellner, M.W.; von Schulthess, G.K.; Kaufmann, P.A.; Burger, I.A.; Messerli, M. Automated detection of lung cancer at ultralow dose PET/CT by deep neural networks - Initial results. Lung Cancer, 2018, 126, 170-173.
[http://dx.doi.org/10.1016/j.lungcan.2018.11.001] [PMID: 30527183]
[56]
Asuntha, A.; Srinivasan, A. Deep learning for lung Cancer detection and classification. Multimedia Tools Appl., 2020, 79, 7731-7762.
[http://dx.doi.org/10.1007/s11042-019-08394-3]
[57]
Zhang, Q.; Kong, X. Design of automatic lung nodule detection system based on multi-scene deep learning framework. IEEE Access, 2020, 8, 90380-90389.
[http://dx.doi.org/10.1109/ACCESS.2020.2993872]
[58]
Toğaçar, M.; Burhan, E.; 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]
[59]
Wilson, A.C.; Roelofs, R.; Stern, M.; Srebro, N.; Recht, B. The marginal value of adaptive gradient methods in machine learning. arXiv, 2017, 2017, 1705.08292.
[60]
Ruder, S An overview of gradient descent optimization algorithms arXiv, 2016, 2016, 1609.04747.
[61]
Sertkaya, M.E.; Ergen, B.; Togacar, M. Diagnosis of eye retinal diseases based on convolutional neural networks using optical coherence images. In: Proceedings of the 23rd International Conference Electronics, Palanga, Lithuania, 2019 17-19 June.
[http://dx.doi.org/10.1109/ELECTRONICS.2019.8765579]
[62]
Serj, M.F.; Lavi, B.; Hoff, G.; Valls, D.P A deep convolutional neural network for lung cancer diagnostic arXiv, 2018, 1804.08170.
[63]
Masood, A.; Sheng, B.; Li, P.; Hou, X.; Wei, X.; Qin, J.; Feng, D. Computer-assisted decision support system in pulmonary cancer detection and stage classification on CT images. J. Biomed. Inform., 2018, 79, 117-128.
[http://dx.doi.org/10.1016/j.jbi.2018.01.005] [PMID: 29366586]
[64]
Chon, A.; Balachandar, N.; Lu, P. Deep convolutional neural networks for lung cancer detection; Standford University: Stanford, USA, 2017.
[65]
Ronneberger, O.; Fischer, P.; Brox, T. U-net: Convolutional networks for biomedical image segmentation. arXiv, 2015, 2015, 1505.04597.
[http://dx.doi.org/10.1007/978-3-319-24574-4_28]
[66]
Bhandary, A.; Prabhu, G.A.; Rajinikanth, V.; Thanaraj, K.P.; Satapathy, S.C.; Robbins, D.E.; Shasky, C.; Zhang, Y.; Tavares, J.M.R.S.; Raja, N.S.M. Deep-learning framework to detect lung abnormality–A study with chest X-Ray and lung CT scan images. Pattern Recognit. Lett., 2020, 129, 271-278.
[http://dx.doi.org/10.1016/j.patrec.2019.11.013]
[67]
Zhihu, H.; Leng, J. Analysis of Hu’s moment invariants on image scaling and rotation. In: Proceedings of the 2nd International Conference on Computer Engineering and Technology, Chengdu, China, 2010, 16-18 April.
[68]
Li, X.; Shen, L.; Xie, X.; Huang, S.; Xie, Z.; Hong, X.; Yu, J. Multi-resolution convolutional networks for chest X-ray radiograph based lung nodule detection. Artif. Intell. Med., 2020, 103, 101744.
[http://dx.doi.org/10.1016/j.artmed.2019.101744] [PMID: 31732411]
[69]
Li, X.; Luo, S.; Hu, Q.; Li, J.; Wang, D. Rib suppression in chest radiographs for lung nodule enhancement. In: Proceedings of the International Conference on Information and Automation, Lijiang, China, 2015, 8-10 Aug.
[http://dx.doi.org/10.1109/ICInfA.2015.7279257]
[70]
Li, X.; Luo, S.; Hu, Q.; Li, J.; Wang, D.; Chiong, F. Automatic lung field segmentation in X-ray radiographs using statistical shape and appearance models. J. Med. Imaging Health Inform., 2016, 6(2), 338-348.
[http://dx.doi.org/10.1166/jmihi.2016.1714]
[71]
Shakeel, P.M.; Burhanuddin, M.A.; Desa, M.I. Automatic lung cancer detection from CT image using improved deep neural network and ensemble classifier. Neural Comput. Appl., 2020.
[http://dx.doi.org/10.1007/s00521-020-04842-6]
[72]
Zhang, X.; Wang, S. Efficient data hiding with histogram-preserving property. Telecomm. Syst., 2012, 49(2), 179-185.
[http://dx.doi.org/10.1007/s11235-010-9364-5]
[73]
Tsai, C.W.; Huang, B.C.; Chiang, M.C. A Novel Spiral Optimization for Clustering. In: Mobile, Ubiquitous, and Intelligent Computing. Lecture Notes in Electrical Engineering; Park, J.; Adeli, H.; Park, N.; Woungang, I., Eds.; Springer: Berlin, Heidelberg, 2014; Vol. 274, .
[http://dx.doi.org/10.1007/978-3-642-40675-1_92]
[74]
Moradi, P.; Jamzad, M. Detecting lung cancer lesions in CT images using 3D convolutional neural networks. In: Proceedings of the 4th International Conference on Pattern Recognition and Image Analysis (IPRIA), 2019 6-7 March, Tehran, Iran.
[http://dx.doi.org/10.1109/PRIA.2019.8785971]
[75]
Heuvelmans, M.A.; van Ooijen, P.M.A.; Ather, S.; Silva, C.F.; Han, D.; Heussel, C.P.; Hickes, W.; Kauczor, H.U.; Novotny, P.; Peschl, H.; Rook, M.; Rubtsov, R.; von Stackelberg, O.; Tsakok, M.T.; Arteta, C.; Declerck, J.; Kadir, T.; Pickup, L.; Gleeson, F.; Oudkerk, M. Lung cancer prediction by Deep Learning to identify benign lung nodules. Lung Cancer, 2021, 154, 1-4.
[http://dx.doi.org/10.1016/j.lungcan.2021.01.027] [PMID: 33556604]
[76]
Tekade, R.; Rajeswari, K. Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India, 2018 16-18 Aug.
[77]
Armato, S.G., III; McLennan, G.; Bidaut, L.; McNitt-Gray, M.F.; Meyer, C.R.; Reeves, A.P.; Zhao, B.; Aberle, D.R.; Henschke, C.I.; Hoffman, E.A.; Kazerooni, E.A.; MacMahon, H.; Van Beeke, E.J.; Yankelevitz, D.; Biancardi, A.M.; Bland, P.H.; Brown, M.S.; Engelmann, R.M.; Laderach, G.E.; Max, D.; Pais, R.C.; Qing, D.P.; Roberts, R.Y.; Smith, A.R.; Starkey, A.; Batrah, P.; Caligiuri, P.; Farooqi, A.; Gladish, G.W.; Jude, C.M.; Munden, R.F.; Petkovska, I.; Quint, L.E.; Schwartz, L.H.; Sundaram, B.; Dodd, L.E.; Fenimore, C.; Gur, D.; Petrick, N.; Freymann, J.; Kirby, J.; Hughes, B.; Casteele, A.V.; Gupte, S.; Sallamm, M.; Heath, M.D.; Kuhn, M.H.; Dharaiya, E.; Burns, R.; Fryd, D.S.; Salganicoff, M.; Anand, V.; Shreter, U.; Vastagh, S.; Croft, B.Y. The lung image database consortium (LIDC) and image database resource initiative (IDRI): A completed reference database of lung nodules on CT scans. Med. Phys., 2011, 38(2), 915-931.
[http://dx.doi.org/10.1118/1.3528204] [PMID: 21452728]
[78]
Setio, A.A.A.; Traverso, A.; de Bel, T.; Berens, M.S.N.; Bogaard, C.V.D.; Cerello, P.; Chen, H.; Dou, Q.; Fantacci, M.E.; Geurts, B.; Gugten, R.V.; Heng, P.A.; Jansen, B.; de Kaste, M.M.J.; Kotov, V.; Lin, J.Y.; Manders, J.T.M.C.; Sóñora-Mengana, A.; García-Naranjo, J.C.; Papavasileiou, E.; Prokop, M.; Saletta, M.; Schaefer-Prokop, C.M.; Scholten, E.T.; Scholten, L.; Snoeren, M.M.; Torres, E.L.; Vandemeulebroucke, J.; Walasek, N.; Zuidhof, G.C.A.; Ginneken, B.V.; Jacobs, C. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge. Med. Image Anal., 2017, 42, 1-13.
[http://dx.doi.org/10.1016/j.media.2017.06.015] [PMID: 28732268]
[79]
Liao, F.; Chen, X.; Hu, X.; Song, S. Estimation of the volume of the left ventricle from MRI images using deep neural networks. IEEE Trans. Cybern., 2019, 49(2), 495-504.
[http://dx.doi.org/10.1109/TCYB.2017.2778799] [PMID: 29990055]
[80]
Badran, E.F.; Mahmoud, E.G.; Hamdy, N. An algorithm for detecting brain tumors in MRI images. In: Proceedings of the International Conference on Computer Engineering & Systems, Cairo, Egypt, 2010 30 Nov.-2 Dec.
[http://dx.doi.org/10.1109/ICCES.2010.5674887]
[81]
Shanmuga Priya, S.; Saran Raj, S.; Surendiran, B.; Arulmurugaselvi, N. Brain Tumour Detection in MRI Using Deep Learning. In: Evolution in Computational Intelligence; Springer: Singapore, 2021; pp. 395-403.
[http://dx.doi.org/10.1007/978-981-15-5788-0_38]
[82]
Joshi, R.; Shan, S. Pixel-level feature space modeling and brain tumor detection using machine learning. In: Proceedings of the 19th IEEE International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA, 2020, 14-17 Dec.
[http://dx.doi.org/10.1109/ICMLA51294.2020.00134]
[83]
Gurbină, M.; Lascu, M.; Lascu, D. Tumor detection and classification of MRI brain image using different wavelet transforms and support vector machines. In: Proceedings of the 42nd International Conference on Telecommunications and Signal Processing (TSP), Budapest, Hungary, 2019, 1-3 July.
[http://dx.doi.org/10.1109/TSP.2019.8769040]
[84]
Simard, P.Y.; Steinkraus, D.; Platt, J.C. Best practices for convolutional neural networks applied to visual document analysis. In: Proceedings of the Seventh International Conference on Document Analysis and Recognition, 2003 6-6 Aug, Edinburgh, UK.
[http://dx.doi.org/10.1109/ICDAR.2003.1227801]
[85]
Varuna Shree, N.; Kumar, T.N.R. Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain Inform., 2018, 5(1), 23-30.
[http://dx.doi.org/10.1007/s40708-017-0075-5] [PMID: 29313301]
[86]
Al-Ayyoub, M.; Ghaith, H.; Omar, D.; Ahmad, A. Machine learning approach for brain tumor detection. 2012.
[http://dx.doi.org/10.1145/2222444.2222467]
[87]
Amin, J.; Sharif, M.; Raza, M.; Saba, T.; Anjum, M.A. Brain tumor detection using statistical and machine learning method. Comput. Methods Programs Biomed., 2019, 177, 69-79.
[http://dx.doi.org/10.1016/j.cmpb.2019.05.015] [PMID: 31319962]
[88]
Cabria, I.; Iker, G. MRI segmentation fusion for brain tumor detection. Inf. Fusion, 2017, 36, 1-9.
[http://dx.doi.org/10.1016/j.inffus.2016.10.003]
[89]
Liu, L.; Fieguth, P.; Guo, Y.; Wang, X.; Pietikäinen, M. Local binary features for texture classification: Taxonomy and experimental study. Pattern Recognit., 2017, 62, 135-160.
[http://dx.doi.org/10.1016/j.patcog.2016.08.032]
[90]
Nooshin, N.; Kubat, M. Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features. Comput. Electr. Eng., 2015, 45, 286-301.
[http://dx.doi.org/10.1016/j.compeleceng.2015.02.007]
[91]
Nasir, M.; Attique Khan, M.; Sharif, M.; Lali, I.U.; Saba, T.; Iqbal, T. An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach. Microsc. Res. Tech., 2018, 81(6), 528-543.
[http://dx.doi.org/10.1002/jemt.23009] [PMID: 29464868]
[92]
Díaz-Pernas, F.J.; Martínez-Zarzuela, M.; Antón-Rodríguez, M.; González-Ortega, D. A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network. Healthcare (Basel), 2021, 9(2), 153.
[http://dx.doi.org/10.3390/healthcare9020153] [PMID: 33540873]
[93]
Hemanth, G.; Janardhan, M.; Sujihelen, L. Design and implementing brain tumor detection using machine learning approach. 3rd International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, 2019, 23-25 April, .
[http://dx.doi.org/10.1109/ICOEI.2019.8862553]
[94]
Rehman, A.; Khan, M.A.; Saba, T.; Mehmood, Z.; Tariq, U.; Ayesha, N. Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture. Microsc. Res. Tech., 2021, 84(1), 133-149.
[http://dx.doi.org/10.1002/jemt.23597] [PMID: 32959422]
[95]
Khan, M.A.; Ashraf, I.; Alhaisoni, M.; Damaševičius, R.; Scherer, R.; Rehman, A.; Bukhari, S.A.C. Multimodal brain tumor classification using deep learning and robust feature selection: A machine learning application for radiologists. Diagnostics (Basel), 2020, 10(8), 565.
[http://dx.doi.org/10.3390/diagnostics10080565] [PMID: 32781795]
[96]
Sadad, T.; Rehman, A.; Munir, A.; Saba, T.; Tariq, U.; Ayesha, N.; Abbasi, R. Brain tumor detection and multi-classification using advanced deep learning techniques. Microsc. Res. Tech., 2021, 84(6), 1296-1308.
[http://dx.doi.org/10.1002/jemt.23688] [PMID: 33400339]
[97]
Saba, T.; Mohamed, A.S.; El-Affendi, M.; Amin, J.; Sharif, M. Brain tumor detection using fusion of hand crafted and deep learning features. Cogn. Syst. Res., 2020, 59, 221-230.
[http://dx.doi.org/10.1016/j.cogsys.2019.09.007]
[98]
Argenziano, G.; Soyer, H.P. Dermoscopy of pigmented skin lesions--a valuable tool for early diagnosis of melanoma. Lancet Oncol., 2001, 2(7), 443-449.
[http://dx.doi.org/10.1016/S1470-2045(00)00422-8] [PMID: 11905739]
[99]
R D, S.; A, S. Deep learning based skin lesion segmentation and classification of melanoma using support vector machine (SVM). Asian Pac. J. Cancer Prev., 2019, 20(5), 1555-1561.
[http://dx.doi.org/10.31557/APJCP.2019.20.5.1555] [PMID: 31128062]
[100]
Codella, N.C.F.; Nguyen, Q.B.; Pankanti, S.; Gutman, D.A.; Helba, B.; Halpern, A.C.; Smith, J.R. Deep learning ensembles for melanoma recognition in dermoscopy images. IBM J. Res. Dev., 2017, 61, 4-5.
[http://dx.doi.org/10.1147/JRD.2017.2708299]
[101]
He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for image recognition. arXiv, 2016, 2016, 1512.03385.
[http://dx.doi.org/10.1109/CVPR.2016.90]
[102]
Gautam, D.; Ahmed, M.; Meena, Y.K.; Ul Haq, A. Machine learning-based diagnosis of melanoma using macro images. Int. J. Numer. Methods Biomed. Eng., 2018, 34(5), e2953.
[http://dx.doi.org/10.1002/cnm.2953] [PMID: 29266819]
[103]
Nock, R.; Nielsen, F. Statistical region merging. IEEE Trans. Pattern Anal. Mach. Intell., 2004, 26(11), 1452-1458.
[http://dx.doi.org/10.1109/TPAMI.2004.110] [PMID: 15521493]
[104]
Glaister, J.; Amelard, R.; Wong, A.; Clausi, D.A. MSIM: multistage illumination modeling of dermatological photographs for illumination-corrected skin lesion analysis. IEEE Trans. Biomed. Eng., 2013, 60(7), 1873-1883.
[http://dx.doi.org/10.1109/TBME.2013.2244596] [PMID: 23380843]
[105]
Peng, H.; Long, F.; Ding, C. Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27(8), 1226-1238.
[http://dx.doi.org/10.1109/TPAMI.2005.159] [PMID: 16119262]
[106]
Kingsly, A.A.S.; Sankaragomathi, B. Performance analysis of machine learning based classifiers for the diagnosis of melanoma cancer and comparison. J. Comput. Theor. Nanosci., 2018, 15(2), 558-575.
[http://dx.doi.org/10.1166/jctn.2018.7124]
[107]
Ramya, V.J.; Navarajan, J.; Prathipa, R.; Kumar, L.A. Detection of melanoma skin cancer using digital camera images. ARPN J. Eng. Appl. Sci., 2015, 10(7), 3082-3085.
[108]
Al-amri, S.S.; Kalyankar, N.V.; Khamitkar, S.D. Linear and non-linear contrast enhancement image. Int. J. Comput. Sci. Netw., 2010, 10(2), 139-143.
[109]
Lagendijk, R.L.; Biemond, J. Basic methods for image restoration and identification. In: The essential guide to image processing; Bovik, Al, Ed.; Academic Press: USA, 2009; pp. 323-348.
[http://dx.doi.org/10.1016/B978-0-12-374457-9.00014-7]
[110]
Premaladha, J.; Ravichandran, K.S. Novel approaches for diagnosing melanoma skin lesions through supervised and deep learning algorithms. J. Med. Syst., 2016, 40(4), 96.
[http://dx.doi.org/10.1007/s10916-016-0460-2] [PMID: 26872778]
[111]
Paniagua, L.R.B.; Correa, D.N.L.; Pinto-Roa, D.; Noguera, J.L.V.; Toledo, L.A.S. Computerized medical diagnosis of melanocytic lesions based on the ABCD approach. CLEI Electr. J., 2016, 19(2), 6-6.
[http://dx.doi.org/10.19153/cleiej.19.2.5]
[112]
Aima, A.; Sharma, A.K. Predictive approach for melanoma skin cancer detection using CNN. In: Proceedings of International Conference on Sustainable Computing in Science, Technology and Management (SUSCOM), Amity University Rajasthan Jaipur-India, 2019, 26-28 Feb.
[http://dx.doi.org/10.2139/ssrn.3352407]
[113]
Monika, M.K.; Vignesh, N.A.; Kumari, C.U.; Kumar, M.N.V.S.S.; Lydia, E.L. Skin cancer detection and classification using machine learning. Mater. Today Proc., 2020, 33, 4266-4270.
[http://dx.doi.org/10.1016/j.matpr.2020.07.366]
[114]
Lopez, A.R.; Giro-i-Nieto, X.; Burdick, J.; Marques, O. Skin lesion classification from dermoscopic images using deep learning techniques. In: Proceedings of the 13th IASTED International Conference on Biomedical Engineering (BioMed), Innsbruck, Austria, 2017, 20-21 Feb.
[http://dx.doi.org/10.2316/P.2017.852-053]
[115]
Nasr-Esfahani, E.; Samavi, S.; Karimi, N.; Soroushmehr, S.M.R.; Jafari, M.H.; Ward, K.; Najarian, K. Melanoma detection by analysis of clinical images using convolutional neural network. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., 2016, 1373-1376.
[http://dx.doi.org/10.1109/EMBC.2016.7590963]
[116]
Ameri, A. A deep learning approach to skin cancer detection in dermoscopy images. J. Biomed. Phys. Eng., 2020, 10(6), 801-806.
[http://dx.doi.org/10.31661/jbpe.v0i0.2004-1107] [PMID: 33364218]
[117]
Krizhevsky, A.; Sutskever, I.; Hinton, G.E. ImageNet classification with deep convolutional neural networks. Commun. ACM, 2017, 60(6), 84-90.
[http://dx.doi.org/10.1145/3065386]
[118]
Saba, T.; Khan, M.A.; Rehman, A.; Marie-Sainte, S.L. Region extraction and classification of skin cancer: A heterogeneous framework of deep CNN features fusion and reduction. J. Med. Syst., 2019, 43(9), 289.
[http://dx.doi.org/10.1007/s10916-019-1413-3] [PMID: 31327058]
[119]
Sylvain, P.; Hasinoff, S.W.; Kautz, J. Local laplacian filters: Edge-aware image processing with a Laplacian pyramid. ACM Trans. Graph., 2011, 30(4), 68.
[http://dx.doi.org/10.1145/2010324.1964963]
[120]
Lei, B.; Kim, J.; Ahn, E.; Kumar, A.; Feng, D.; Fulham, M. Step-wise integration of deep class-specific learning for dermoscopic image segmentation. Pattern Recognit., 2019, 85, 78-89.
[http://dx.doi.org/10.1016/j.patcog.2018.08.001]
[121]
Wu, J.T.; Dernoncourt, F.; Gehrmann, S.; Tyler, P.D.; Moseley, E.T.; Carlson, E.T.; Grant, D.W.; Li, Y.; Welt, J.; Celi, L.A. Behind the scenes: A medical natural language processing project. Int. J. Med. Inform., 2018, 112, 68-73.
[http://dx.doi.org/10.1016/j.ijmedinf.2017.12.003] [PMID: 29500024]
[122]
Codella, N.C.F.; Gutman, D.; Celebi, M.E.; Helba, B.; Marchetti, M.A.; Dusza, S.W.; Kalloo, A.; Liopyris, K.; Mishra, N.; Kittler, H.; Halpern, A. Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC), IEEE. In: Proceedings of the 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA, 2018, 4-7 April, .
[http://dx.doi.org/10.1109/ISBI.2018.8363547]
[123]
Li, Y.; Shen, L. Skin lesion analysis towards melanoma detection using deep learning network. Sensors (Basel), 2018, 18(2), 556.
[http://dx.doi.org/10.3390/s18020556] [PMID: 29439500]
[124]
Dai, X.; Spasić, I.; Meyer, B.; Chapman, S.; Andres, F. Machine learning on mobile: An on-device inference app for skin cancer detection. In: Proceedings of the Fourth International Conference on Fog and Mobile Edge Computing (FMEC); , 2019; pp. 301-305.
[http://dx.doi.org/10.1109/FMEC.2019.8795362]
[125]
Jojoa Acosta, M.F.; Caballero Tovar, L.Y.; Garcia-Zapirain, M.B.; Percybrooks, W.S. Melanoma diagnosis using deep learning techniques on dermatoscopic images. BMC Med. Imaging, 2021, 21(1), 6.
[http://dx.doi.org/10.1186/s12880-020-00534-8] [PMID: 33407213]
[126]
Ali, M.S.; Miah, M.S.; Haque, J.; Rahman, M.M.; Islam, M.K. An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models. Machine Learn. Appl., 2021, 5, 100036.
[http://dx.doi.org/10.1016/j.mlwa.2021.100036]

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