Acknowledgement
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2021-2017-0-01630) supervised by the IITP (Institute for Information & communications Technology Promotion), and by the Technology development Program (S2797147) funded by the Ministry of SMEs and Startups (MSS, korea).
References
- Korea Central Cancer Registry, National Cancer Center. Annual report of cancer statistics in Korea in 2017. Ministry of Health and Welfare. 2019; 33.
- http://opendata.hira.or.kr/op/opc/olap3thDsInfo.do. Accessed on 23 Feb 2021.
- https://www.hira.or.kr/bbsDummy.do;INTERSESSIONID=g4BoNr8ikGPGbzbX8V4OWHwPnvCxfaVCw8Ef9JZb9LzudJguEp37!1206362664!375626493?pgmid=HIRAA020041000100&brdScnBltNo=4&brdBltNo=9774. Accessed on 15 Feb 2021.
- https://www.cancer.gov/types/cervical/patient/cervical-treatment-pdq. Accessed on 5 Feb 2021.
- Coppleson, LW. Barry Brown. Estimation of the screening error rate from the observed detection rates in repeated cervical cytology. American Journal of Obstetrics and Gynecology. 1974;119(7):953-958. https://doi.org/10.1016/0002-9378(74)90013-1
- Zarchi MK, Binesh F, Kazemi Z, Teimoori S, Soltani HR, Chiti Z. Value of colposcopy in the early diagnosis of cervical cancer in patients with abnormal pap smears at Shahid Sadoughi Hospital, Yazd. Asian Pacific Journal of Cancer Prevention. 2011;12(12):3439-3441.
- https://www.ksog.org/public/index.php?sub=4. Accessed on 3 Feb 2021.
- Alyafeai Z, Ghouti L. A fully-automated deep learning pipeline for cervical cancer classification. Expert Systems with Applications. 2020;141.
- Wu M, Yan C, Liu H, Liu Q, Yin Y. Automatic classification of cervical cancer from cytological images by using convolutional neural network. Bioscience Reports. 2018;38(6):1-9.
- Kudva V, Prasad K, Guruvare S. Transfer Learning for Classification of Uterine Cervix Images for Cervical Cancer Screening. Lecture Notes in Electrical Engineering. 2020; 614:299-312. https://doi.org/10.1007/978-981-15-0626-0_25
- Lange H. Automatic detection of multi-level acetowhite regions in RGB color images of the uterine cervix. Medical Imaging 2005: Image Processing. 2005;5747:1004. https://doi.org/10.1117/12.596064
- Lee Y, Kim HJ, Kim GB, Kim N. Deep Learning-based Feature Extraction for Medical Image Analysis. 2014;1-12.
- Jeelani H, Martin J, Vasquez F, Salerno M, Weller DS. Image quality affects deep learning reconstruction of MRI. International Symposium on Biomedical Imaging. 2018;357-360.
- Guo P, Singh S, Xue Z, Long R, Antani S. Deep learning for assessing image focus for automated cervical cancer screening. 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019. 2019;2019-2022.
- Ulku EE, Camurcu AY. Computer aided brain tumor detection with histogram equalization and morphological image processing techniques. ICECCO 2013. 2013;48-51.
- Ullah Z, Farooq MU, Lee SH, An D. A hybrid image enhancement based brain MRI images classification technique. Medical Hypotheses. 2020;143:109922. https://doi.org/10.1016/j.mehy.2020.109922
- Ziaei A, Yeganeh H, Faez K, Sargolzaei S. A novel approach for contrast enhancement in biomedical images based on histogram equalization. BMEI 2008. 2008;1:855-858.
- Suralkar SR, Rajput, S. Enhancement of Images Using Contrast Image Enhancement Techniques. International Journal of Recent Advances in Engineering & Technology. 2020;8(3):16-20. https://doi.org/10.46564/ijraet.2020.v08i03.004
- Singh RP, Dixit M. Histogram Equalization: A Strong Technique for Image Enhancement. International Journal of Signal Processing, Image Processing and Pattern Recognition. 2015;8(8):345-352. https://doi.org/10.14257/ijsip.2015.8.8.35
- Gonzalez RC, Woods RE. Digital Image Processing 3rd Edition (Issue 3). New Jersey, Pearson Education, Inc. 2008;394-460.
- Russo F. Piecewise Linear Model-Based Image Enhancement. EURASIP J. Adv. Signal Process. 2004;983173.
- https://docs.opencv.org/3.4/d4/d1b/tutorial_histogram_equalization.html. Accessed on 17 Feb 2021.
- Yun JW. Deep Residual Learning for Image Recognition arXiv:1512.03385v1. Enzyme and Microbial Technology. 1996;19(2):107-117. https://doi.org/10.1016/0141-0229(95)00188-3
- Kurt B, Nabiyev VV, Turhan K. Medical images enhancement by using anisotropic filter and CLAHE. INISTA 2012 - International Symposium on INnovations in Intelligent SysTems and Applications. 2012;1-4.
- Kim M. Feature Extraction on a Periocular Region and Person Authentication Using a ResNet Model. Journal of Korea Multimedia Society. 2019;22(1):1347-1355
- Weiss K, Khoshgoftaar TM, Wang DD. A survey of transfer learning. In Journal of Big Data. Springer International Publishing. 2016;3(1).
- Hore A, Ziou D. Image quality metrics: PSNR vs. SSIM. International Conference on Pattern Recognition. 2010;2366-2369.
- Kang K, Lee J. PSNR Appraisal of MRI image. 2009;3(4): 13-20.
- Wang Z, Bovik AC, Sheikh HR, Simoncelli E.P. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing. 2004;13(4):600-612 https://doi.org/10.1109/TIP.2003.819861
- Nevriyanto A, Equalization AH. Enhancement, and Standard Median Filter (Noise Removal) with Pixel-Based and Human Visual System-Based Measurements. International Conference on Electrical Engineering and Computer Science. 2017;1(1): 114-119.
- Kugelman J, Alonso-Caneiro D, Read SA, Vincent SJ, Chen FK, Collins MJ. Effect of Altered OCT Image Quality on Deep Learning Boundary Segmentation. IEEE Access. 2020; 8:43537-43553. https://doi.org/10.1109/ACCESS.2020.2977355
- Kaur H, Rani J. MRI brain image enhancement using Histogram Equalization techniques. Proceedings of the 2016 IEEE International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2016. 2016;1: 770-773.
- Srinivasan Y, Hernes D, Tulpule B, Yang S, Guo J, Mitra S, Yagneswaran S, Nutter B, Jeronimo J, Phillips B, Long R, Ferris D. A probabilistic approach to segmentation and classification of neoplasia in uterine cervix images using color and geometric features. Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2005;5747(2):995-1003.