Generic placeholder image

Current Genomics


ISSN (Print): 1389-2029
ISSN (Online): 1875-5488

Review Article

Advancement in Deep Learning Methods for Diagnosis and Prognosis of Cervical Cancer

Author(s): Akshat Gupta, Alisha Parveen, Abhishek Kumar and Pankaj Yadav*

Volume 23, Issue 4, 2022

Published on: 17 June, 2022

Page: [234 - 245] Pages: 12

DOI: 10.2174/1389202923666220511155939

Price: $65


Cervical cancer is the leading cause of death in women, mainly in developing countries, including India. Recent advancements in technologies could allow for more rapid, cost-effective, and sensitive screening and treatment measures for cervical cancer. To this end, deep learning-based methods have received importance for classifying cervical cancer patients into different risk groups. Furthermore, deep learning models are now available to study the progression and treatment of cancerous cervical conditions. Undoubtedly, deep learning methods can enhance our knowledge toward a better understanding of cervical cancer progression. However, it is essential to thoroughly validate the deep learning-based models before they can be implicated in everyday clinical practice. This work reviews recent development in deep learning approaches employed in cervical cancer diagnosis and prognosis. Further, we provide an overview of recent methods and databases leveraging these new approaches for cervical cancer risk prediction and patient outcomes. Finally, we conclude the state-of-the-art approaches for future research opportunities in this domain.

Keywords: Deep learning, cervical cancer, diagnosis, neural networks, risk prediction, sensitive screening.

Graphical Abstract
WHO Cervical cancer. Available from:. (Accessed 3 on: 2022 Jan 2).
Burd, E.M. Human papillomavirus and cervical cancer. Clin. Microbiol. Rev., 2003, 16(1), 1-17.
[] [PMID: 12525422]
Yeo-Teh, N.S.L.; Ito, Y.; Jha, S. High-risk human papillomaviral oncogenes E6 and E7 target key cellular pathways to achieve oncogenesis. Int. J. Mol. Sci., 2018, 19(6), 1706.
[] [PMID: 29890655]
Mello, V. Renee K. Sundstrom. Cervical Intraepithelial Neoplasia; StatPearls Publishing: USA, 2021.
Kim, E.; Huang, X. A Data Driven Approach to Cervigram Image Analysis and Classification; Springer Netherlands, 2013, pp. 1-13.
Magrina, J.F.; Zanagnolo, V.L. Robotic surgery for cervical cancer. Yonsei Med. J., 2008, 49(6), 879-885.
[] [PMID: 19108008]
Zhong, S.; Zhang, K.; Bagheri, M.; Burken, J.G.; Gu, A.; Li, B. Machine learning: New ideas and tools in environmental science and engineering Environ. Sci. Technol 2021, 2021, acs.est.1c01339.
Raza, A.; Bardhan, S.; Xu, L.; Yamijala, S.S.R.K.C.; Lian, C.; Kwon, H.; Wong, B.M. A machine learning approach for predicting defluorination of Per- and Polyfluoroalkyl Substances (PFAS) for their efficient treatment and removal. Environ. Sci. Technol. Lett., 2019, 6(10), 624-629.
Kourou, K.; Exarchos, T.P.; Exarchos, K.P.; Karamouzis, M.V.; Fotiadis, D.I. Machine learning applications in cancer prognosis and prediction. Comput. Struct. Biotechnol. J., 2014, 13, 8-17.
[] [PMID: 25750696]
Cao, C.; Liu, F.; Tan, H.; Song, D.; Shu, W.; Li, W.; Zhou, Y.; Bo, X.; Xie, Z. Deep learning and its applications in biomedicine. Genom. Proteom. Bioinf., 2018, 16(1), 17-32.
[] [PMID: 29522900]
Intel & MobileODT. Cervical Cancer Screening Available from: on: 2021 Jul 18)
Christian, R. Using data from cervical cancer risk classification., Available from: on: 2021 Jul 16)
USAID. Cervical cancer, measure evaluation, family planning and reproductive health indicators database. Available from: on 2021 Jul 21)
Agarwal, S.M.; Raghav, D.; Singh, H.; Raghava, G.P.S. CCDB: A curated database of genes involved in cervix cancer. Nucleic Acids Res., 2011, 39, D975-D979.
Rygaard, C. The Danish quality database for cervical cancer screening. Clin. Epidemiol., 2016, 8, 655-660.
[] [PMID: 27826216]
Zhou, L.; Zheng, W.; Luo, M.; Feng, J.; Jin, Z.; Wang, Y.; Zhang, D.; Tang, Q.; He, Y. dbCerEx: A web-based database for the analysis of cervical cancer transcriptomes. PLoS One, 2014, 9(6), e99834.
[] [PMID: 24918550]
Min, S.; Lee, B.; Yoon, S. Deep learning in bioinformatics. Brief. Bioinform., 2017, 18(5), 851-869.
[PMID: 27473064]
Glattfelder, J.B. The consciousness of reality; Springer, 2019, pp. 515-595.
Sarker, I.H. Deep learning: A comprehensive overview on techniques, taxonomy, applications and research directions. SN Comput. Sci., 2021, 2(6), 420.
[] [PMID: 34426802]
Yamashita, R.; Nishio, M.; Do, R.K.G.; Togashi, K. Convolutional neural networks: An overview and application in radiology. Insights Imaging, 2018, 9(4), 611-629.
[] [PMID: 29934920]
Salehinejad, H; Sankar, S; Barfett, J; Colak, E; Valaee, S Recent advances in recurrent neural networks., 2017.
Pathania, D.; Landeros, C.; Rohrer, L.; D’Agostino, V.; Hong, S.; Degani, I.; Avila-Wallace, M.; Pivovarov, M.; Randall, T.; Weissleder, R.; Lee, H.; Im, H.; Castro, C.M. Point-of-care cervical cancer screening using deep learning-based microholography. Theranostics, 2019, 9(26), 8438-8447.
[] [PMID: 31879529]
Park, Y.R.; Kim, Y.J.; Ju, W.; Nam, K.; Kim, S.; Kim, K.G. Comparison of machine and deep learning for the classification of cervical cancer based on cervicography images. Sci. Rep., 2021, 11(1), 16143.
[] [PMID: 34373589]
Jusman, Y.; Ng, S.C.; Abu Osman, N.A. Intelligent screening systems for cervical cancer. Sci. World J.,, 2014, 2014, 810368.
[] [PMID: 24955419]
Alyafeai, Z.; Ghouti, L. A fully-automated deep learning pipeline for cervical cancer classification. Expert Syst. Appl., 2020, 141, 112951.
Sompawong, N.; Mopan, J.; Pooprasert, P.; Himakhun, W.; Suwannarurk, K.; Ngamvirojcharoen, J. Automated pap smear cervical cancer screening using deep learning. IEEE Eng. Med. Biol. Soc. Ann. Int. Conf. 2019, 2019, pp. 7044-8.
Xu, T.; Zhang, H.; Huang, X.; Zhang, S.; Metaxas, D.N. Multimodal deep learning for cervical dysplasia diagnosis.2016, 2016, 115-23.
Wentzensen, N.; Lahrmann, B.; Clarke, M.A.; Kinney, W.; Tokugawa, D.; Poitras, N.; Locke, A.; Bartels, L.; Krauthoff, A.; Walker, J.; Zuna, R.; Grewal, K.K.; Goldhoff, P.E.; Kingery, J.D.; Castle, P.E.; Schiffman, M.; Lorey, T.S.; Grabe, N. Accuracy and efficiency of deep-learning-based automation of dual stain cytology in cervical cancer screening. J. Natl. Cancer Inst., 2021, 113(1), 72-79.
[] [PMID: 32584382]
Chandran, V.; Sumithra, M.G.; Karthick, A.; George, T.; Deivakani, M.; Elakkiya, B.; Subramaniam, U.; Manoharan, S. Diagnosis of cervical cancer based on ensemble deep learning network using colposcopy images. BioMed Res. Int., 2021, 2021, 5584004.
[] [PMID: 33997017]
Adweb, K.M.A.; Cavus, N.; Sekeroglu, B. Cervical cancer diagnosis using very deep networks over different activation functions. IEEE Access, 2021, 9, 46612-46625.
Jiang, X.; Li, J.; Kan, Y.; Yu, T.; Chang, S.; Sha, X.; Zheng, H.; Luo, Y.; Wang, S. MRI based radiomics approach with deep learning for prediction of vessel invasion in early-stage cervical cancer. IEEE/ACM Trans. Comput. Biol. Bioinf., 2021, 18(3), 995-1002.
[] [PMID: 31905143]
Yu, S.; Feng, X.; Wang, B.; Dun, H.; Zhang, S.; Zhang, R.; Huang, X. Automatic classification of cervical cells using deep learning method. IEEE Access, 2021, 9, 32559-32568.
Singh, S.K.; Goyal, A. A stack autoencoders based deep neural network approach for cervical cell classification in pap-smear images. Recent Adv. Comput. Sci. Commun., 2021, 14(1), 62-70.
Tan, X.; Li, K.; Zhang, J.; Wang, W.; Wu, B.; Wu, J.; Li, X.; Huang, X. Automatic model for cervical cancer screening based on convolutional neural network: A retrospective, multicohort, multicenter study. Cancer Cell Int., 2021, 21(1), 35.
[] [PMID: 33413391]
Rahaman, M.M.; Li, C.; Yao, Y.; Kulwa, F.; Wu, X.; Li, X.; Wang, Q. DeepCervix: A deep learning-based framework for the classification of cervical cells using hybrid deep feature fusion techniques. Comput. Biol. Med., 2021, 136, 104649.
[] [PMID: 34332347]
Mehmood, M.; Rizwan, M.; Gregus, M.; Abbas, S. Machine learning assisted cervical cancer detection. Front. Public Health, 2021, 2021, 9.
Manna, A.; Kundu, R.; Kaplun, D.; Sinitca, A.; Sarkar, R. A fuzzy rank-based ensemble of CNN models for classification of cervical cytology. Sci. Rep., 2021, 11(1), 14538.
[] [PMID: 34267261]
Yan, Y.; Zhao, K.; Cao, J.; Ma, H. Prediction research of cervical cancer clinical events based on recurrent neural network. Proc. Comput. Sci., 2021, 183, 221-229.
Sridevi, A.K.; Adhish, P.; Sreeram, M. Cervical cancer detection using Convolutional Neural Network(CNN) and Long-Short Term Memory(LSTM) based on histopathological images. Ann. Rom. Soc. Cell Biol., 2021, 25(06), 5875-5883.

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy