Interpretable CRAM‑Enhanced Lightweight Dual‑Branch CNN for Real‑Time Breast Cancer Histopathology in Internet‑of‑Medical‑Things Environments

ABSTRACT Breast cancer remains a primary global health concern, with histopathological image analysis serving as the diagnostic gold standard. However, manual microscopy is time‐consuming and often subjective. While deep learning offers a powerful solution, existing models are typically too complex and opaque for real‐time use in Internet of Medical Things (IoMT) environments. To address this, we propose an interpretable and lightweight hybrid deep learning model that combines MobileNetV2 and EfficientNet‐B0, enhanced by a novel contextual recurrent attention module (CRAM). CRAM refines fused features through attention‐based weighting, improving focus on diagnostically relevant regions. The model achieved 99.9% classification accuracy and an AUC of 1.00, outperforming standalone baselines while remaining efficient (∼12 M parameters) and suitable for IoMT deployment. Interpretability is ensured through integrated Grad‐CAM and SHAP analyses, which visually and quantitatively explain predictions by highlighting malignant tissue features that align with pathologist judgment. This balance of accuracy, efficiency, and transparency enables real‐time, trustworthy diagnostics for resource‐limited and point‐of‐care settings. Future work includes extending to multi‐class tumor subtypes and clinical validation in real‐world workflows. The proposed system represents a significant step toward making AI in digital pathology more accessible and explainable.