Evaluating Deep Learning Models for Breast Cancer Classification: A Comparative Study

This study evaluates the effectiveness of deep learning models in classifying histopathological images for early and accurate detection of breast cancer. Eight advanced models, including ResNet-50, DenseNet-121, ResNeXt-50, Vision Transformer (ViT), GoogLeNet (Inception v3), EfficientNet, MobileNet, and SqueezeNet, were compared using a dataset of 277,524 image patches. The Vision Transformer (ViT) model, with its attention-based mechanisms, achieved the highest validation accuracy of 94%, outperforming conventional CNNs. The study demonstrates the potential of advanced machine learning methods to enhance precision and efficiency in breast cancer diagnosis in clinical settings.

@article{eskandari2025_2408.16859,
  title={ Evaluating Deep Learning Models for Breast Cancer Classification: A Comparative Study },
  author={ Sania Eskandari and Ali Eslamian and Nusrat Munia and Amjad Alqarni and Qiang Cheng },
  journal={arXiv preprint arXiv:2408.16859},
  year={ 2025 }
}