Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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A Performance Comparison of Histogram Equalization Algorithms for Cervical Cancer Classification Model

평활화 알고리즘에 따른 자궁경부 분류 모델의 성능 비교 연구

  • Kim, Youn Ji (Department of Biomedical Engineering, Gachon University) ;
  • Park, Ye Rang (Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University) ;
  • Kim, Young Jae (Department of Biomedical Engineering, Gachon University) ;
  • Ju, Woong (Department of Obstetrics & Gynecology, Ewha Womans University Seoul Hospital) ;
  • Nam, Kyehyun (Department of Obstetrics & Gynecology, Soonchunhyang University, Bucheon Hospital) ;
  • Kim, Kwang Gi (Department of Biomedical Engineering, Gachon University)
  • 김윤지 (가천대학교 의용생체공학과) ;
  • 박예랑 (가천대학교 융합의과학과) ;
  • 김영재 (가천대학교 의용생체공학과) ;
  • 주웅 (이화여자대학교 산부인과) ;
  • 남계현 (순천향대학교 부천병원 산부인과) ;
  • 김광기 (가천대학교 의용생체공학과)
  • Received : 2021.03.11
  • Accepted : 2021.06.28
  • Published : 2021.06.30
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Abstract

We developed a model to classify the absence of cervical cancer using deep learning from the cervical image to which the histogram equalization algorithm was applied, and to compare the performance of each model. A total of 4259 images were used for this study, of which 1852 images were normal and 2407 were abnormal. And this paper applied Image Sharpening(IS), Histogram Equalization(HE), and Contrast Limited Adaptive Histogram Equalization(CLAHE) to the original image. Peak Signal-to-Noise Ratio(PSNR) and Structural Similarity index for Measuring image quality(SSIM) were used to assess the quality of images objectively. As a result of assessment, IS showed 81.75dB of PSNR and 0.96 of SSIM, showing the best image quality. CLAHE and HE showed the PSNR of 62.67dB and 62.60dB respectively, while SSIM of CLAHE was shown as 0.86, which is closer to 1 than HE of 0.75. Using ResNet-50 model with transfer learning, digital image-processed images are classified into normal and abnormal each. In conclusion, the classification accuracy of each model is as follows. 90.77% for IS, which shows the highest, 90.26% for CLAHE and 87.60% for HE. As this study shows, applying proper digital image processing which is for cervical images to Computer Aided Diagnosis(CAD) can help both screening and diagnosing.

Keywords

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).

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