RGB Channel Superposition Algorithm with Acetowhite Mask Images in a Cervical Cancer Classification Deep Learning Model
- 주제(키워드) acetowhite , cervical cancer , deep learning , ResNet , RGB channel superposition
- 등재 SCIE, SCOPUS
- OA유형 All Open Access, Gold, Green
- 발행기관 MDPI
- 발행년도 2022
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000191009
- 본문언어 영어
- Published As https://doi.org/10.3390/s22093564
- PubMed https://pubmed.ncbi.nlm.nih.gov/35591254
초록/요약
Cervical cancer is one of the main causes of death from cancer in women. However, it can be treated successfully at an early stage. This study aims to propose an image processing algorithm based on acetowhite, which is an important criterion for diagnosing cervical cancer, to increase the accuracy of the deep learning classification model. Then, we mainly compared the performance of the model, the original image without image processing, a mask image made with acetowhite as the region of interest, and an image using the proposed algorithm. In conclusion, the deep learning classification model based on images with the proposed algorithm achieved an accuracy of 81.31%, which is approximately 9% higher than the model with original images and approximately 4% higher than the model with acetowhite mask images. Our study suggests that the proposed algorithm based on acetowhite could have a better performance than other image processing algorithms for classifying stages of cervical images. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
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