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A malignant tumor that appears in the large intestine is known as colon cancer, and it’s said to be among the three major cancers globally affecting people, with more than 1.9 million new cases being reported every year alongside about 935,000 dying due to this type of cancer yearly. The earlier the detection and intervention, the better the chances of survival of the patient. This study aims to characterize the deep learning models VGG16 and VGG19 for feature extraction of benign and malignant colon cancer images. Further classifying extracted features are KNN, Decision Tree, Naïve Bayes, SVM-RBF, Random Forest, AdaBoost and XGBoost. The findings suggest that SVM-RBF topped the list when combined with VGG16 and VGG19 at accuracy rates of 85.78% and 87.19%, respectively.

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Performance Analysis of Visual Geometry Group Features with Different Classification Models for Identification of Colon Cancer from Histopathological Images

  • 会议论文

  • 发表日期：

    2026年

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  • Gowri Shankar Manivannan,
  • Karthikeyan Shanmugam,
  • Harikumar Rajaguru,
  • S. Rajanna

摘要

A malignant tumor that appears in the large intestine is known as colon cancer, and it’s said to be among the three major cancers globally affecting people, with more than 1.9 million new cases being reported every year alongside about 935,000 dying due to this type of cancer yearly. The earlier the detection and intervention, the better the chances of survival of the patient. This study aims to characterize the deep learning models VGG16 and VGG19 for feature extraction of benign and malignant colon cancer images. Further classifying extracted features are KNN, Decision Tree, Naïve Bayes, SVM-RBF, Random Forest, AdaBoost and XGBoost. The findings suggest that SVM-RBF topped the list when combined with VGG16 and VGG19 at accuracy rates of 85.78% and 87.19%, respectively.