평활화 알고리즘에 따른 자궁경부 분류 모델의 성능 비교 연구A Performance Comparison of Histogram Equalization Algorithms for Cervical Cancer Classification Model
- Other Titles
- A Performance Comparison of Histogram Equalization Algorithms for Cervical Cancer Classification Model
- Authors
- 김윤지; 박예랑; 김영재; 주웅; 남계헌; 김광기
- Issue Date
- Jun-2021
- Publisher
- 대한의용생체공학회
- Keywords
- Cervical cancer; Histogram equalization; Classification; ResNet-50
- Citation
- 의공학회지, v.42, no.3, pp 80 - 85
- Pages
- 6
- Journal Title
- 의공학회지
- Volume
- 42
- Number
- 3
- Start Page
- 80
- End Page
- 85
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/20855
- ISSN
- 1229-0807
2288-9396
- 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 Equaliza�tion(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 proces
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Collections - College of Medicine > Department of Obstetrics and Gynecology > 1. Journal Articles
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