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arXiv · 2408.16859

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

Sania Eskandari·Ali Eslamian·Nusrat Munia·Amjad Alqarni·Qiang Cheng
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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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Sania Eskandari, Ali Eslamian, Nusrat Munia, Amjad Alqarni, Qiang Cheng. 2024-08-29. Evaluating Deep Learning Models for Breast Cancer Classification: A Comparative Study. https://doi.org/10.1117/12.3047441

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