Abstract: Introduction Breast cancer is the most commonly occurring cancer in women globally, and histopathological biopsy analysis stands out as the gold standard technique for diagnosing breast cancer at a tissue level. The problem of automatically classifying histopathological images is challenging due to varying morphology across tissue subtypes, inconsistent staining methods from different laboratories, and the wide variety of diagnoses possible at different magnification levels. This study proposes a hybrid and explainable deep learning architecture for breast cancer classification based on the BreakHis histopathological....
Key Word: BreakHis Dataset, Breast Cancer Histopathology, Deep Learning, EfficientNet-B4, Explainable AI, Histological Subtype Classification, Hybrid Architecture, Vision Transformer
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