Evaluating Deep Learning Models for Breast Cancer Classification: A Comparative Study
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 18 references that are in the map. First posted on arXiv 29 Aug 2024.
Builds on
6 of 18 references.
References · 6
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
- 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
- 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,546
- 2017End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Designcited by 88
- 2024Vision Mamba for Classification of Breast Ultrasound Imagescited by 7
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