Benchmarking Deep Learning Models for Breast Cancer Detection: A Comparison of Vision Transformers and CNNs

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ResNet-50 achieves the highest accuracy of 93.62%, outperforming other models in breast cancer detection and image classification.
The study uses a dataset of 9,248 breast cancer images, evaluating accuracy, precision, recall, and F1-score across different deep learning architectures.
Vision transformers, while showing promise, lag slightly with 87.46% accuracy compared to traditional CNNs, indicating a need for optimization.
The findings suggest that CNNs, particularly ResNet-50, are currently the most effective models for breast cancer classification within this dataset.

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This study compares Vision Transformers and CNNs for breast cancer detection, indicating effectiveness differences in accuracy metrics.

Demiroğlu et al. (2025) studied this question.

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