Do Foundation Models Outperform Lightweight CNNs in Breast Cancer Histopathology Classification?

Accurate and computationally efficient classification of breast cancer histopathology images is critical for scalable clinical deployment of AI-assisted diagnostics. This paper benchmarks three deep learning architectures, MobileNetV3, ResNet50, and DINO Vision Transformer (ViT), on the BreakHis dataset across six dimensions: test accuracy, weighted F1-score, inference latency, memory usage, energy consumption, and model size. Using 7,909 microscopic images across four magnification factors (40X