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This research paper presents a deep learning approach for breast cancer detection using mammograms. A Convolutional Neural Network (CNN) model is developed to classify mammogram images as either benign or malignant. The model is trained and evaluated on a mammographic dataset, achieving an accuracy of 90.24% on the test set. The study also evaluates the model’s performance using various metrics, including precision, recall, F1-score, and AUC. Additionally, a visual analysis of the model’s predictions is presented, demonstrating its capability to accurately classify mammogram images. The findings highlight the potential of deep learning models, such as CNNs, in enhancing the accuracy of breast cancer detection.

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Breast Cancer Detection from Mammograms Using Deep Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Mohammed Chibi,
  • Wafae Abbaoui,
  • Amine Zouir,
  • Aya Loudiyi Lamghafri,
  • Oussama Essamadi,
  • Wajih Rhalem,
  • Najib Al Idrissi,
  • Soumia Ziti

摘要

This research paper presents a deep learning approach for breast cancer detection using mammograms. A Convolutional Neural Network (CNN) model is developed to classify mammogram images as either benign or malignant. The model is trained and evaluated on a mammographic dataset, achieving an accuracy of 90.24% on the test set. The study also evaluates the model’s performance using various metrics, including precision, recall, F1-score, and AUC. Additionally, a visual analysis of the model’s predictions is presented, demonstrating its capability to accurately classify mammogram images. The findings highlight the potential of deep learning models, such as CNNs, in enhancing the accuracy of breast cancer detection.