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. 2022 May 7;22(9):3564.
doi: 10.3390/s22093564.

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

Yoon Ji Kim  1 , Woong Ju  2 , Kye Hyun Nam  3 , Soo Nyung Kim  4 , Young Jae Kim  1 , Kwang Gi Kim  1   5
Affiliations

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

Yoon Ji Kim et al. Sensors (Basel). .

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.

Keywords: RGB channel superposition; ResNet; acetowhite; cervical cancer; deep learning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
A schematic diagram of the RGB channel superposition process. (a) Acetowhite mask image (b) Original image (c) RGB channel superposition image.
Figure 2
Figure 2
Nine cases of cervical images made through RGB channel superposition.
Figure 3
Figure 3
Diagram of the ResNet 50 model architecture.
Figure 4
Figure 4
Critical Difference Diagram of the Friedman–Nemenyi test for deep learning model performance comparison. The number shows the lank of three models. The lower the rank, the better the performance of a model.
Figure 5
Figure 5
ROC graph and AUC of the original, acetowhite mask, and RGB channel superposition models.
See this image and copyright information in PMC

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