SCIE, EI, Scopus, INSPEC, DBLP, CSCD, etc.
Citation: | Hua Chen, Juan Liu, Qing-Man Wen, Zhi-Qun Zuo, Jia-Sheng Liu, Jing Feng, Bao-Chuan Pang, Di Xiao. CytoBrain: Cervical Cancer Screening System Based on Deep Learning Technology[J]. Journal of Computer Science and Technology, 2021, 36(2): 347-360. DOI: 10.1007/s11390-021-0849-3 |
[1] |
Jemal A, Center M M, DeSantis C, Ward E M. Global patterns of cancer incidence and mortality rates and trends. Cancer Epidemiology Biomarkers and Prevention, 2010, 19(8):1893-1907. DOI: 10.1158/1055-9965.EPI-10-0437.
|
[2] |
Prat J, Franceschi S. Cancers of the female reproductive organs. In World Cancer Report 2014, Stewart B W, Wild C P (eds.), International Agency for Research on Cancer, 2014, pp.465-481.
|
[3] |
Adem K, Kiliçarslan S, Cömert O. Classification and diagnosis of cervical cancer with stacked autoencoder and softmax classification. Expert Systems with Applications, 2019, 115:557-564. DOI: 10.1016/j.eswa.2018.08.050.
|
[4] |
Kurnianingsih, Allehaibi K H S, Nugroho L E, Widyawan, Lazuardi L, Prabuwono A S, Mantoro T. Segmentation and classification of cervical cells using deep learning. IEEE Access, 2019, 7(99):116925-116941. DOI: 10.1109/ACCESS.2019.2936017.
|
[5] |
Bray F, Ferlay J, Soerjomataram I, Siegel R L, Torre L A, Jemal A. Global cancer statistics 2018:GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA:A Cancer Journal for Clinicians, 2018, 68(6):394-424. DOI: 10.3322/caac.21492.
|
[6] |
Wittet S, Goltz S, Cody A. Progress in cervical cancer prevention:The CCA report card. Technical Report, Cervical Cancer Action, 2011. https://path.azureedge.net/media/documents/RHccareportcard.pdf, Mar. 2020.
|
[7] |
Schwaiger C, Aruda M, Lacoursiere S, Rubin R. Current guidelines for cervical cancer screening. Journal of the American Academy of Nurse Practitioners, 2012, 24(7):417-424. DOI: 10.1111/j.1745-7599.2012.00704.x.
|
[8] |
William W, Ware A, Basaza-Ejiri A H, Obungoloch J. A review of image analysis and machine learning techniques for automated cervical cancer screening from pap-smear images. Computer Methods and Programs in Biomedicine, 2018, 164:15-22. DOI: 10.1016/j.cmpb.2018.05.034.
|
[9] |
William W, Ware A, Basaza-Ejiri A H, Obungoloch J. Cervical cancer classification from Pap-smears using an enhanced fuzzy C-means algorithm. Informatics in Medicine Unlocked, 2019, 14:23-33. DOI: 10.1016/j.imu.2019.02.001.
|
[10] |
Bora K, Chowdhury M, Mahanta L B, Kundu M K, Das A K. Automated classification of Pap smear images to detect cervical dysplasia. Computer Methods and Programs in Biomedicine, 2017, 138:31-47. DOI: 10.1016/j.cmpb.2016.10.001.
|
[11] |
McDonald J T, Kennedy S. Cervical cancer screening by immigrant and minority women in Canada. Journal of Immigrant and Minority Health, 2007, 9(4):323-334. DOI: 10.1007/s10903-007-9046-x.
|
[12] |
Elsheikh T M, Austin R M, Chhieng D F, Miller F S, Moriarty A T, Renshaw A A. American society of cytopathology workload recommendations for automated Pap test screening:Developed by the productivity and quality assurance in the era of automated screening task force. Diagnostic Cytopathology, 2013, 41(2):174-178. DOI: 10.1002/dc.22817.
|
[13] |
Yamal J M, Guillaud M, Atkinson E N, Follen M, MacAulay C, Cantor S B, Cox D D. Prediction using hierarchical data:Applications for automated detection of cervical cancer. Statistical Analysis and Data Mining, 2015, 8(2):65-74. DOI: 10.1002/sam.11261.
|
[14] |
Su J, Xu X, He Y, Song J. Automatic detection of cervical cancer cells by a two-level cascade classification system. Analytical Cellular Pathology, 2016, 2016:Article No. 9535027. DOI: 10.1155/2016/9535027.
|
[15] |
Kurniawati Y E, Permanasari A E, Fauziati S. Comparative study on data mining classification methods for cervical cancer prediction using pap smear results. In Proc. the 1st International Conference on Biomedical Engineering, Oct. 2016. DOI: 10.1109/IBIOMED.2016.7869827.
|
[16] |
Sharma M, Singh S K, Agrawal P, Madaan V. Classification of clinical dataset of cervical cancer using KNN. Indian Journal of Science and Technology, 2016, 9(28):1-5. DOI: 10.17485/ijst/2016/v9i28/98380.
|
[17] |
Liu Y, Zhang P, Song Q, Li A, Zhang P, Gui Z. Automatic segmentation of cervical nuclei based on deep learning and a conditional random field. IEEE Access, 2018, 6:53709-53721. DOI: 10.1109/ACCESS.2018.2871153.
|
[18] |
Wang P, Wang L, Li Y, Song Q, Lv S, Hu X. Automatic cell nuclei segmentation and classification of cervical Pap smear images. Biomedical Signal Processing and Control, 2019, 48:93-103. DOI: 10.1016/j.bspc.2018.09.008.
|
[19] |
Gupta R, Sarwar A, Sharma V. Screening of cervical cancer by artificial intelligence based analysis of digitized Papanicolaou-smear images. International Journal of Contemporary Medical Research, 2017, 4(5):1108-1113. DOI: 10.21276/ijcmr.
|
[20] |
Wu W, Zhou H. Data-driven diagnosis of cervical cancer with support vector machine-based approaches. IEEE Access, 2017, 5:25189-25195. DOI: 10.1109/ACCESS.2017.2763984.
|
[21] |
Zhang L, Lu L, Nogues I, Summers R M, Liu S, Yao J. DeepPap:Deep convolutional networks for cervical cell classification. IEEE Journal of Biomedical and Health Informatics, 2017, 21(6):1633-1643. DOI: 10.1109/JBHI.2017.2705583.
|
[22] |
Nayar R, Wilbur D C. The Bethesda System for Reporting Cervical Cytology:Definitions, Criteria, and Explanatory Notes (3rd edition). Springer, 2015.
|
[23] |
Bay H, Tuytelaars T, Gool L V. SURF:Speeded up robust features. In Proc. the 9th European Conference on Computer Vision, May 2006, pp.404-417. DOI: 10.1007/1174402332.
|
[24] |
Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, 1979, 9(1):62-66. DOI: 10.1109/TSMC.1979.4310076.
|
[25] |
Soille P. Morphological Image Analysis:Principles and Applications (2nd edition). Springer-Verlag Berlin Heidelberg Publisher, 2003. DOI: 10.1007/978-3-662-05088-0.
|
[26] |
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556, 2014. https://arxiv.org/pdf/1409.1556.pdf, Mar. 2020.
|
[27] |
Wang J, Perez L. The effectiveness of data augmentation in image classification using deep learning. arXiv:1712.04621, 2017. https://arxiv.org/pdf/1712.04621.pdf, Mar. 2020.
|
[28] |
Son J, Shin J Y, Kim H D, Jung K H, Park K H, Park S J. Development and validation of deep learning models for screening multiple abnormal findings in retinal fundus images. Ophthalmology, 2020, 127(1):85-94. DOI: 10.1016/j.ophtha.2019.05.029.
|
[29] |
Chalakkal R J, Abdulla W H, Thulaseedharan S S. Quality and content analysis of fundus images using deep learning. Computers in Biology and Medicine, 2019, 108:317-331. DOI: 10.1016/j.compbiomed.2019.03.019.
|
[30] |
Ba J L, Kiros J R, Hinton G E. Layer normalization. arXiv:1607.06450, 2016. https://arxiv.org/pdf/160-7.06450.pdf, Mar. 2020.
|
[31] |
Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proc. the 14th International Joint Conference on Artificial Intelligence, Aug. 1995, pp.1137-1143.
|
[32] |
Refaeilzadeh P, Tang L, Liu H. Cross-validation. In Encyclopedia of Database Systems, Liu L, Özsu M T (eds.), Springer, 2016, pp.532-538. DOI: 10.1007/978-0-387-39940-9565.
|
[33] |
Zeiler M D. ADADELTA:An adaptive learning rate method. arXiv:1212.5701, 2012. https://arxiv.org/pdf/1212.5701.pdf, Mar. 2020.
|
[34] |
Xiang S, Liang Q, Hu Y, Tang P, Coppola G, Zhang D, Sun W. AMC-Net:Asymmetric and multi-scale convolutional neural network for multi-label HPA classification. Computer Methods and Programs in Biomedicine, 2019, 178:275-287. DOI: 10.1016/j.cmpb.2019.07.009.
|
[35] |
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout:A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 2014, 15:1929-1958.
|
[36] |
Nielsen M. Improving the way neural networks learn. http://neuralnetworksanddeeplearning.com/chap3.html, Mar. 2020.
|
[37] |
Wang Y, Shen X, Yang Y. The classification of Chinese sensitive information based on BERT-CNN. In Artificial Intelligence Algorithms and Applications, Li K, Li W, Wang H, Liu Y (eds.), Springer, 2020, pp.269-280. DOI: 10.1007/978-981-15-5577-020.
|
[38] |
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. arXiv:1512.00567, 2015. https://arxiv.org/pdf/15-12.00567.pdf, Mar. 2020.
|
[39] |
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In Proc. the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Jun. 2016, pp.770-778. DOI: 10.1109/CVPR.2016.90.
|
[40] |
Huang G, Liu Z, Maaten L V D, Weinberger K Q. Densely connected convolutional networks. In Proc. the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Jul. 2017, pp.2261-2269. DOI: 10.1109/CVPR.2017.243.
|
[41] |
Paul P R, Bhowmik M K, Bhattacharjee D. Automated cervical cancer detection using Pap smear images. In Proc. the 4th International Conference on Soft Computing for Problem Solving, Dec. 2014, pp.267-278. DOI: 10.1007/978-81-322-2217-023.
|
[42] |
Plissiti M E, Dimitrakopoulos P, Sfikas G, Nikou C, Krikoni O, Charchanti A. SIPaKMeD:A new dataset for feature and image based classification of normal and pathological cervical cells in Pap smear images. In Proc. the 25th IEEE International Conference on Image Processing, Oct. 2018, pp.3144-3148. DOI: 10.1109/ICIP.2018.8451588.
|