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<p>Cervical cancer ranks fourth globally in terms of both incidence and mortality among women, making timely diagnosis essential for effective treatment. Although the acetowhite regions and their margins are important for cervical cancer staging, their potential for automated cancer grading remains underexplored. This study aimed to enhance diagnostic accuracy and grading precision by effectively analyzing the acetowhite region and its surroundings. Using four classifiers (Logistic Regression(LR), Random Forest(RF), XGBoost(XGB), and Support Vector Machine(SVM)), 464 cervical images (228 atypical and 236 positive cases) were analyzed. From a set of 75 features, the classifiers identified the top 5 based on feature importance. Receiver Operating Characteristic (ROC) analysis yielded the following precisions for models trained with masks containing only the acetowhite lesion: LR 0.80 (CI 95% 0.70–0.90), SVM 0.83 (CI 95% 0.75–0.92), RF 0.79 (CI 95% 0.69–0.89), XGB 0.66 (CI 95% 0.55–0.77). For models trained with masks including the acetowhite lesion and a 10-pixel margin: LR 0.79 (CI 95% 0.70–0.88), SVM 0.87 (CI 95% 0.78–0.95), RF 0.86 (CI 95% 0.77–0.94), XGB 0.84 (CI 95% 0.75–0.93). Our findings indicate that including a 10-pixel margin around acetowhite lesions improves classifier performance, suggesting its advantage in the automated classification of cervical images.</p>

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Comparison of performance of cervical cancer grading based on acetowhite areas

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月2日

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  • Won Jin Yang,
  • So Hyeon Lee,
  • Young Jae Kim,
  • Kwang Gi Kim

摘要

Cervical cancer ranks fourth globally in terms of both incidence and mortality among women, making timely diagnosis essential for effective treatment. Although the acetowhite regions and their margins are important for cervical cancer staging, their potential for automated cancer grading remains underexplored. This study aimed to enhance diagnostic accuracy and grading precision by effectively analyzing the acetowhite region and its surroundings. Using four classifiers (Logistic Regression(LR), Random Forest(RF), XGBoost(XGB), and Support Vector Machine(SVM)), 464 cervical images (228 atypical and 236 positive cases) were analyzed. From a set of 75 features, the classifiers identified the top 5 based on feature importance. Receiver Operating Characteristic (ROC) analysis yielded the following precisions for models trained with masks containing only the acetowhite lesion: LR 0.80 (CI 95% 0.70–0.90), SVM 0.83 (CI 95% 0.75–0.92), RF 0.79 (CI 95% 0.69–0.89), XGB 0.66 (CI 95% 0.55–0.77). For models trained with masks including the acetowhite lesion and a 10-pixel margin: LR 0.79 (CI 95% 0.70–0.88), SVM 0.87 (CI 95% 0.78–0.95), RF 0.86 (CI 95% 0.77–0.94), XGB 0.84 (CI 95% 0.75–0.93). Our findings indicate that including a 10-pixel margin around acetowhite lesions improves classifier performance, suggesting its advantage in the automated classification of cervical images.