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However, manual microscopy is time\u2010consuming and often subjective. While deep learning offers a powerful solution, existing models are typically too complex and opaque for real\u2010time 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\u2010B0, enhanced by a novel contextual recurrent attention module (CRAM). CRAM refines fused features through attention\u2010based 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 (\u223c12\u00a0M parameters) and suitable for IoMT deployment. Interpretability is ensured through integrated Grad\u2010CAM 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\u2010time, trustworthy diagnostics for resource\u2010limited and point\u2010of\u2010care settings. Future work includes extending to multi\u2010class tumor subtypes and clinical validation in real\u2010world workflows. The proposed system represents a significant step toward making AI in digital pathology more accessible and explainable.<\/jats:p>","DOI":"10.1002\/smll.202509066","type":"journal-article","created":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T07:19:06Z","timestamp":1773904746000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Interpretable CRAM\u2011Enhanced Lightweight Dual\u2011Branch CNN for Real\u2011Time Breast Cancer Histopathology in Internet\u2011of\u2011Medical\u2011Things Environments"],"prefix":"10.1002","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2592-2824","authenticated-orcid":false,"given":"Roseline Oluwaseun","family":"Ogundokun","sequence":"first","affiliation":[{"name":"Department of Multimedia Engineering Kaunas University of Technology  Kaunas Lithuania"},{"name":"Department of Computer Systems Engineering Tshwane University of Technology  Pretoria South Africa"},{"name":"Department of Computer Science Redeemer's University  Ede Osun Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rotimi\u2010Williams","family":"Bello","sequence":"additional","affiliation":[{"name":"Department of Computer Systems Engineering Tshwane University of Technology  Pretoria South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pius Adewale","family":"Owolawi","sequence":"additional","affiliation":[{"name":"Department of Computer Systems Engineering Tshwane University of Technology  Pretoria South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rytis","family":"Maskeli\u016bnas","sequence":"additional","affiliation":[{"name":"Department of Software Engineering Kaunas University of Technology  Kaunas Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulsatar Abduljabbar","family":"Sultan","sequence":"additional","affiliation":[{"name":"Business Management Department Catholic University in Erbil  Erbil Kurdistan Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,3,19]]},"reference":[{"key":"e_1_2_14_2_1","doi-asserted-by":"publisher","DOI":"10.1142\/S1793962324410095"},{"key":"e_1_2_14_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/s23020656"},{"key":"e_1_2_14_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010025\u201004136\u20108"},{"key":"e_1_2_14_5_1","doi-asserted-by":"crossref","unstructured":"J. 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