RGB Channel Superposition Algorithm with Acetowhite Mask Images in a Cervical Cancer Classification Deep Learning Model

Yoon Ji Kim, Woong Ju, Kye Hyun Nam, Soo Nyung Kim, Young Jae Kim, Kwang Gi Kim

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number3564
JournalSensors (Switzerland)
Volume22
Issue number9
DOIs
StatePublished - 1 May 2022

Keywords

  • RGB channel superposition
  • ResNet
  • acetowhite
  • cervical cancer
  • deep learning

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