EVC-Net: A Hybrid Deep Learning Network for Breast Cancer Classification from Histopathological Images

Breast cancer is a prevalent and life-threatening disease where early and accurate diagnosis is critical for effective treatment. Conventional histopathological analysis, while the standard for diagnosis, can be laborious and is subject to inter-observer variability, highlighting the need for robust automated methods. This paper introduces EVC-Net, a novel hybrid deep learning framework designed to automate the classification of breast cancer from histopathological images. EVC-Net synergistically integrates an EfficientNetV2S for fine-grained texture feature extraction, a vision transformer (ViT) for capturing global context, and a capsule network to preserve spatial hierarchies within tissue structures. The proposed model is evaluated on the public BreakHis dataset. Across all four magnification levels, EVC-Net demonstrates robust performance, achieving an average accuracy of 0.985 and an AUC-ROC of 0.994 for binary (benign vs. malignant) classification. For the eight-subtype multi-class task, the model maintains high efficacy, attaining an average accuracy of 0.954 and an AUC-ROC of 0.980. Furthermore, interpretability analysis using Grad-CAM is conducted, generating heatmaps overlay visualization to understand the rational behind model's predictions. These results demonstrate the potential of the EVC-Net framework to enhance diagnostic accuracy and consistency, offering valuable support for clinical workflows in oncology.