Title Interpretable CRAM‑enhanced lightweight dual‑branch CNN for real‑time breast cancer histopathology in Internet‑of‑Medical‑Things environments
Authors Ogundokun, Roseline Oluwaseun ; Bello, Rotimi-Williams ; Owolawi, Pius Adewale ; Maskeliūnas, Rytis ; Sultan, Abdulsatar Abduljabbar
DOI 10.1002/smll.202509066
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Is Part of Small.. Weinheim : Wiley-VCH. 2026, Early access, art. no. e09066, p. 1-25.. ISSN 1613-6810. eISSN 1613-6829
Keywords [eng] breast cancer ; histopathology ; internet‑of‑medical‑things ; interpretable ; lightweight deep learning ; smart diagnostic systems
Abstract [eng] 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.
Published Weinheim : Wiley-VCH
Type Journal article
Language English
Publication date 2026
CC license CC license description