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

• URL: http://arxiv.org/abs/2408.16859v2
• Date: Thu, 08 May 2025 13:34:48 GMT
• Title: Evaluating Deep Learning Models for Breast Cancer Classification: A Comparative Study
• Authors: Sania Eskandari, Ali Eslamian, Nusrat Munia, Amjad Alqarni, Qiang Cheng,
• Abstract summary: The Vision Transformer (ViT) model, with its attention-based mechanisms, achieved the highest validation accuracy of 94%.<n>The study demonstrates the potential of advanced machine learning methods to enhance precision and efficiency in breast cancer diagnosis in clinical settings.
• Score: 9.392940888377423
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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