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This research study uses Grad-CAM to improve model interpretability and compares the performance of VGGNet-19 in breast cancer diagnosis from mammograms. Grad-CAM heatmaps identify the most significant areas in the images for the model’s classification, giving insight into its decision-making process. The research compares VGGNet-19’s accuracy on a mammography dataset, showing its high diagnostic performance. With the integration of Grad-CAM, radiologists and AI systems will be able to work more collaboratively, resulting in more accurate and quicker clinical breast cancer diagnosis.

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Breast Cancer Detection with Enhanced Interpretability Using VGGNet-19 and Grad-CAM

  • 会议论文

  • 发表日期：

    2026年

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  • Pentapati Kalyan Babu,
  • T. Bala Ravi Teja,
  • C. H. Pavan Kumar,
  • O. Satısh Varma,
  • T. Shanmukha Mouli Eswar

摘要

This research study uses Grad-CAM to improve model interpretability and compares the performance of VGGNet-19 in breast cancer diagnosis from mammograms. Grad-CAM heatmaps identify the most significant areas in the images for the model’s classification, giving insight into its decision-making process. The research compares VGGNet-19’s accuracy on a mammography dataset, showing its high diagnostic performance. With the integration of Grad-CAM, radiologists and AI systems will be able to work more collaboratively, resulting in more accurate and quicker clinical breast cancer diagnosis.