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A total of 2.3 million cases of breast cancer are discovered globally each year, making it one of the top causes of fatalities for women. Early diagnosis and intervention are critical for improving patient outcomes. There are several ways to classify breast cancer. There's been a recent spike in interest in deep learning algorithms for the classification of breast cancer because of their superior prediction accuracy over traditional techniques. This work uses a dataset with two classes of pictures to train several pretrained CNN models. The models underwent processing utilizing several hyperparameters. With the greatest accuracy of 99.99%, the Xception transfer learning model outperformed the others.

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Deep Learning for Breast Cancer Classification: A Comparative Study of Pretrained CNN Models

  • 会议论文

  • 发表日期：

    2025年

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  • Aneri Pandya,
  • Nesh Rochwani,
  • Rudra Patel,
  • Hemant Yadav,
  • Nirav Bhatt

摘要

A total of 2.3 million cases of breast cancer are discovered globally each year, making it one of the top causes of fatalities for women. Early diagnosis and intervention are critical for improving patient outcomes. There are several ways to classify breast cancer. There's been a recent spike in interest in deep learning algorithms for the classification of breast cancer because of their superior prediction accuracy over traditional techniques. This work uses a dataset with two classes of pictures to train several pretrained CNN models. The models underwent processing utilizing several hyperparameters. With the greatest accuracy of 99.99%, the Xception transfer learning model outperformed the others.