Login Register Cart 0
Generic placeholder image

Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Recognition of Cervical Precancerous Lesions Based on Probability Distribution Feature Guidance

Author(s): Jun Liu, Xiaoxue Sun, Rihui Li and Yuanxiu Peng*

Volume 18, Issue 11, 2022

Published on: 28 June, 2022

Article ID: e280422204177 Pages: 10

DOI: 10.2174/1573405618666220428104541

Price: $65

Abstract

Introduction: Cervical cancer is a high incidence of cancer in women and cervical precancerous screening plays an important role in reducing the mortality rate.

Methods: In this study, we proposed a multichannel feature extraction method based on the probability distribution features of the Acetowhite (AW) region to identify cervical precancerous lesions, with the overarching goal to improve the accuracy of cervical precancerous screening. A k-means clustering algorithm was first used to extract the cervical region images from the original colposcopy images. We then used a deep learning model called DeepLab V3+ to segment the AW region of the cervical image after the acetic acid experiment, from which the probability distribution map of the AW region after segmentation was obtained. This probability distribution map was fed into a neural network classification model for multichannel feature extraction, which resulted in the final classification performance.

Results: Results of the experimental evaluation showed that the proposed method achieved an average accuracy of 87.7%, an average sensitivity of 89.3%, and an average specificity of 85.6%. Compared with the methods that did not add segmented probability features, the proposed method increased the average accuracy rate, sensitivity, and specificity by 8.3%, 8%, and 8.4%, respectively.

Conclusion: Overall, the proposed method holds great promise for enhancing the screening of cervical precancerous lesions in the clinic by providing the physician with more reliable screening results that might reduce their workload.

Keywords: Acetic acid test, colposcopy image, cervical screening, deep learning, automatic diagnosis, cervical cancer.

Graphical Abstract

[1]
Sabik LM, Tarazi WW, Hochhalter S, Dahman B, Bradley CJ. Medicaid expansions and cervical cancer screening for low-income women. Health Serv Res 2018; 53(Suppl. 1): 2870-91.
[http://dx.doi.org/10.1111/1475-6773.12732] [PMID: 28664993]
[2]
Brisson M, Drolet M. Global elimination of cervical cancer as a public health problem. Lancet Oncol 2019; 20(3): 319-21.
[http://dx.doi.org/10.1016/S1470-2045(19)30072-5] [PMID: 30795952]
[3]
Alyafeai Z, Ghouti L. A fully-automated deep learning pipeline for cervical cancer classification. Expert Syst Appl 2020; 141: 112951-91.
[http://dx.doi.org/10.1016/j.eswa.2019.112951]
[4]
Arbyn M, Weiderpass E, Bruni L, et al. Estimates of incidence and mortality of cervical cancer in 2018: A worldwide analysis. Lancet Glob Health 2020; 8(2): e191-203.
[http://dx.doi.org/10.1016/S2214-109X(19)30482-6] [PMID: 31812369]
[5]
Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021; 71(3): 209-49.
[http://dx.doi.org/10.3322/caac.21660] [PMID: 33538338]
[6]
Pfaendler KS, Wenzel L, Mechanic MB, Penner KR. Cervical cancer survivorship: Long-term quality of life and social support. Clin Ther 2015; 37(1): 39-48.
[http://dx.doi.org/10.1016/j.clinthera.2014.11.013] [PMID: 25592090]
[7]
Basu P, Meheus F, Chami Y, Hariprasad R, Zhao F, Sankaranarayanan R. Management algorithms for cervical cancer screening and pre-cancer treatment for resource-limited settings. Int J Gynaecol Obstet 2017; 138(Suppl. 1): 26-32.
[http://dx.doi.org/10.1002/ijgo.12183] [PMID: 28691336]
[8]
de Martel C, Plummer M, Vignat J, Franceschi S. Worldwide burden of cancer attributable to HPV by site, country and HPV type. Int J Cancer 2017; 141(4): 664-70.
[http://dx.doi.org/10.1002/ijc.30716] [PMID: 28369882]
[9]
Kessler TA. Cervical cancer: Prevention and early detection. Semin Oncol Nurs 2017; 33(2): 172-83.
[10]
Crothers BA. Cytologic-histologic correlation: Where are we now, and where are we going? Cancer Cytopathol 2018; 126(5): 301-8.
[http://dx.doi.org/10.1002/cncy.21991] [PMID: 29579352]
[11]
Ronco G, Dillner J, Elfström KM, et al. International HPV screening working group. Efficacy of HPV-based screening for prevention of invasive cervical cancer: Follow-up of four European randomised controlled trials. Lancet 2014; 383(9916): 524-32.
[http://dx.doi.org/10.1016/S0140-6736(13)62218-7] [PMID: 24192252]
[12]
Adsul P, Manjunath N, Srinivas V, Arun A, Madhivanan P. Implementing community-based cervical cancer screening programs using visu-al inspection with acetic acid in India: A systematic review. Cancer Epidemiol 2017; 49: 161-74.
[http://dx.doi.org/10.1016/j.canep.2017.06.008] [PMID: 28704717]
[13]
Holme F, Kapambwe S, Nessa A, Basu P, Murillo R, Jeronimo J. Scaling up proven innovative cervical cancer screening strategies: Chal-lenges and opportunities in implementation at the population level in low- and lower-middle-income countries. Int J Gynaecol Obstet 2017; 138(Suppl. 1): 63-8.
[http://dx.doi.org/10.1002/ijgo.12185] [PMID: 28691331]
[14]
Ketelaars PJW, Bosgraaf RP, Siebers AG, et al. High-risk human papillomavirus detection in self-sampling compared to physician-taken smear in a responder population of the Dutch cervical screening: Results of the VERA study. Prev Med 2017; 101: 96-101.
[http://dx.doi.org/10.1016/j.ypmed.2017.05.021] [PMID: 28579497]
[15]
Mezei AK, Armstrong HL, Pedersen HN, et al. Cost-effectiveness of cervical cancer screening methods in low- and middle-income coun-tries: A systematic review. Int J Cancer 2017; 141(3): 437-46.
[http://dx.doi.org/10.1002/ijc.30695] [PMID: 28297074]
[16]
Okatch H, Sowicz TJ, Teng H, Ramogola-Masire D, Buttenheim AM. Achieving public and global health competencies: A teaching case study of Botswana’s cervical cancer screening program. Public Health Nurs 2018; 35(3): 238-45.
[http://dx.doi.org/10.1111/phn.12389] [PMID: 29424085]
[17]
Goceri E, Songul C. Biomedical information technology: Image based computer aided diagnosis systems. International Conference on Ad-vanced Technologies. Antalya. 2018.
[18]
Shin H-C, Orton MR, Collins DJ, Doran SJ, Leach MO. Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data. IEEE Trans Pattern Anal Mach Intell 2013; 35(8): 1930-43.
[http://dx.doi.org/10.1109/TPAMI.2012.277] [PMID: 23787345]
[19]
Suk H-I, Shen D. Deep learning-based feature representation for AD/MCI classification. International conference on medical image computing and computer-assisted intervention. In: Springer; 2013; pp. 583-90.
[20]
Roth HR, Lu L, Seff A, et al. A new 2.5 D representation for lymph node detection using random sets of deep convolutional neural network observations. International conference on medical image computing and computer-assisted intervention. In: Springer; 2014; pp. 520-7.
[21]
Chen L, Shen C, Zhou Z, et al. Accurate segmenting of cervical tumors in PET imaging based on similarity between adjacent slices. Comput Biol Med 2018; 97: 30-6.
[http://dx.doi.org/10.1016/j.compbiomed.2018.04.009] [PMID: 29684783]
[22]
Hao D, Peng J, Wang Y, Liu J, Zhou X, Zheng D. Evaluation of convolutional neural network for recognizing uterine contractions with elec-trohysterogram. Comput Biol Med 2019; 113: 103394-402.
[http://dx.doi.org/10.1016/j.compbiomed.2019.103394] [PMID: 31445226]
[23]
Xu T, Zhang H, Xin C, et al. Multi-feature based benchmark for cervical dysplasia classification evaluation. Pattern Recognit 2017; 63: 468-75.
[http://dx.doi.org/10.1016/j.patcog.2016.09.027] [PMID: 28603299]
[24]
Zhang X, Zhao SG. Cervical image classification based on image segmentation preprocessing and a CapsNet network model. Int J Imaging Syst Technol 2019; 29(1): 19-28.
[http://dx.doi.org/10.1002/ima.22291]
[25]
Goceri E. CapsNet topology to classify tumours from brain images and comparative evaluation. IET Image Process 2019; 14: 882-9.
[http://dx.doi.org/10.1049/iet-ipr.2019.0312]
[26]
Goceri E. Analysis of capsule networks for image classification. 15th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing.
[27]
Goceri E. Capsule neural networks in classification of skin lesions. 15th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing.
[28]
Yue Z, Ding S, Zhao W, et al. Automatic CIN grades prediction of sequential cervigram image using LSTM with multistate CNN features. IEEE J Biomed Health Inform 2020; 24(3): 844-54.
[http://dx.doi.org/10.1109/JBHI.2019.2922682] [PMID: 31199278]
[29]
Hu L, Bell D, Antani S, et al. An observational study of deep learning and automated evaluation of cervical images for cancer screening. J Natl Cancer Inst 2019; 111(9): 923-32.
[http://dx.doi.org/10.1093/jnci/djy225] [PMID: 30629194]
[30]
Li Y, Chen J, Xue P, et al. Computer-aided cervical cancer diagnosis using time-lapsed colposcopic images. IEEE Trans Med Imaging 2020; 39(11): 3403-15.
[http://dx.doi.org/10.1109/TMI.2020.2994778] [PMID: 32406830]
[31]
Goceri E. Analysis of deep networks with residual blocks and different activation functions: Classification of skin diseases. Ninth international conference on image processing theory, tools and applications (IPTA). In: IEEE; 2019; pp. 1-6.
[32]
Peng G, Dong H, Liang T, Li L, Liu J. Diagnosis of cervical precancerous lesions based on multimodal feature changes. Comput Biol Med 2021; 130: 104209-16.
[http://dx.doi.org/10.1016/j.compbiomed.2021.104209] [PMID: 33440316]
[33]
Grenko RT, Abendroth CS, Frauenhoffer EE, Ruggiero FM, Zaino RJ. Variance in the interpretation of cervical biopsy specimens obtained for atypical squamous cells of undetermined significance. Am J Clin Pathol 2000; 114(5): 735-40.
[http://dx.doi.org/10.1309/K7C9-X5P0-001B-2HK5] [PMID: 11068547]
[34]
Phillips KA, Deverka PA, Hooker GW, Douglas MP. Genetic test availability and spending: Where are we now? Where are we going? Health Aff (Millwood) 2018; 37(5): 710-6.
[http://dx.doi.org/10.1377/hlthaff.2017.1427] [PMID: 29733704]
[35]
Goceri E, Dura E. Comparison of weighted K-means clustering approaches. International Conference on Applied Analysis and Mathematical Modeling (ICAAMM2018).
[36]
Greenspan H, Gordon S, Zimmerman G, et al. Automatic detection of anatomical landmarks in uterine cervix images. IEEE Trans Med Imaging 2009; 28(3): 454-68.
[http://dx.doi.org/10.1109/TMI.2008.2007823] [PMID: 19244017]
[37]
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition. 770-8.
[http://dx.doi.org/10.1109/CVPR.2016.90]
[38]
Liu J, Du H, Lu H, Peng Y, Li L, Zhang Y. Comparison of classifier configurations for the classification of cervical intraepithelial neoplasia using acetic acid test images. J Med Imaging Health Inform 2019; 9(6): 1103-11.
[http://dx.doi.org/10.1166/jmihi.2019.2711]
[39]
Bai B, Du Y, Liu P, Sun P, Li P, Lv Y. Detection of cervical lesion region from colposcopic images based on feature reselection. Biomed Signal Process Control 2020; 57: 101785-92.
[http://dx.doi.org/10.1016/j.bspc.2019.101785]
[40]
Liu J, Liang T, Peng Y, et al. Segmentation of acetowhite region in uterine cervical image based on deep learning. 2021; 1-14.
[http://dx.doi.org/10.3233/THC-212890]
[41]
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. Computer vision and pattern recognition 2015; 1409-556.
[42]
Hu J, Shen L, Sun G. Squeeze-and-excitation networks. Proceedings of the IEEE conference on computer vision and pattern recognition. 7132-41.
[43]
Huang S, Gao M, Yang D, Huang X, Elgammal A, Zhang X. Unbalanced graph-based transduction on superpixels for automatic cervigram image segmentation. 12th International Symposium on Biomedical Imaging (ISBI). In: IEEE; 2015; pp. 1556-9.
[44]
Marquez-Grajales A, Acosta-Mesa H-G, Mezura-Montes E, Hernández-Jiménez R. Cervical image segmentation using active contours and evolutionary programming over temporary acetowhite patterns. IEEE Congress on Evolutionary Computation (CEC) 2016; 3863-70.
[http://dx.doi.org/10.1109/CEC.2016.7744279]
[45]
Goceri E. Fully automated and adaptive intensity normalization using statistical features for brain MR images. Celal Bayar University Jour-nal of Science 2018; 14(1): 125-34.
[http://dx.doi.org/10.18466/cbayarfbe.384729]
[46]
Goceri E. Intensity normalization in brain MR images using spatially varying distribution matching. 11th Int Conf on computer graphics, Visualization, 2017 Computer vision and image processing (CGVCVIP 2017). In: Lisbon, Portogual; 2017; pp. 300-4.
[47]
Li Z, Shi W, Xing Q, et al. Low-dose CT image denoising with improving WGAN and hybrid loss function. Comput Math Methods Med 2021; 2021: 2973108.
[http://dx.doi.org/10.1155/2021/2973108] [PMID: 34484414]
[48]
Goceri E. Deep learning based classification of facial dermatological disorders. Comput Biol Med 2021; 128: 104118-64.
[http://dx.doi.org/10.1016/j.compbiomed.2020.104118] [PMID: 33221639]
[49]
GÖÇERİ E. An application for automated diagnosis of facial dermatological diseases. Journal of Izmir Katip Celebi University Faculty of Health Sciences 2021; 6(3): 91-9.
[50]
Goceri E. Diagnosis of skin diseases in the era of deep learning and mobile technology. Comput Biol Med 2021; 134: 104458-71.
[http://dx.doi.org/10.1016/j.compbiomed.2021.104458] [PMID: 34000524]
[51]
Goceri E. Image augmentation for deep learning based lesion classification from skin images. IEEE 4th International Conference on Image Processing, Applications and Systems (IPAS). In: IEEE; 2020; pp. 144-8.

Rights & Permissions Print Cite
Article Metrics
15
1
© 2024 Bentham Science Publishers | Privacy Policy