Abstract Accurate and efficient diagnosis of breast cancer from histopathological images remains a major challenge in clinical practice due to subjective interpretation, inter-observer variability, and labor-intensive manual examination. To address these limitations, this work introduces a transfer learning–based framework for automated breast cancer classification using the Breast Cancer Histology Images (BACH) dataset. Several pre-trained deep architectures—including MobileNet, ResNet variants, EfficientNet, and Vision Transformers—were evaluated and extended with a Multi-Scale Feature Fusion (MSFF) module to capture morphological heterogeneity across spatial resolutions. Among these, the Enhanced MobileNet (E‑MobileNet) with MSFF outperforming recent state‑of‑the‑art models and achieving a classification accuracy of 95%, precision of 95%, recall of 94%, and F1‑score of 96%. The framework was further validated on the BreaKHis dataset across multiple magnifications, achieving an average accuracy of 90.6%. These results confirm the robustness and generalization capability of the proposed model for practical clinical deployment in digital pathology. Similar content being viewed by others Introduction Breast cancer remains one of the most significant health challenges facing women globally, with approximately 13.0% of women expected to be diagnosed with the disease during their lifetime. The disease affects over 4 million women currently living with breast cancer in the United States alone, with an annual incidence rate of 130.8 per 100,000 women and a death rate of 19.2 per 100,000 women per year. Despite advances in treatment, breast cancer continues to be the fourth leading cause of cancer death in the United States, with survival outcomes heavily dependent on early detection and intervention as shown in Fig. 1. The critical importance of early detection is underscored by the substantially higher survival rates achieved when breast cancer is identified and treated during its initial stages, making the development of more accurate and efficient detection methods a paramount healthcare priority1. The standard breast cancer diagnostic workflow