{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T16:13:06Z","timestamp":1781194386962,"version":"3.54.1"},"reference-count":41,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T00:00:00Z","timestamp":1748304000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003819","name":"Natural Science Foundation of Hubei Province","doi-asserted-by":"publisher","award":["2022CFA007"],"award-info":[{"award-number":["2022CFA007"]}],"id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003819","name":"Natural Science Foundation of Hubei Province","doi-asserted-by":"publisher","award":["2023EGA027"],"award-info":[{"award-number":["2023EGA027"]}],"id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hubei Province Centralized Guided Local Science and Technology Development Funds Project","award":["2022CFA007"],"award-info":[{"award-number":["2022CFA007"]}]},{"name":"Hubei Province Centralized Guided Local Science and Technology Development Funds Project","award":["2023EGA027"],"award-info":[{"award-number":["2023EGA027"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Early and accurate breast cancer diagnosis is critical in enhancing patient survival rates, with histopathological image analysis serving as a key diagnostic tool. To address challenges in breast histopathology image analysis, including multi-magnification characteristics, insufficient feature extraction in traditional CNNs, and high inter-class similarity coupled with significant intra-class variation among tumor subtypes, this work proposes a focal cosine-enhanced EfficientNetB0 (FCE-EfficientNetB0) classification model. The framework incorporates a multiscale efficient attention mechanism into a multiscale efficient mobile inverted bottleneck conv, where parallel 1D convolutional branches extract features across magnification levels, while the attention mechanism prioritizes clinically relevant patterns. A focal cosine hybrid loss function further optimizes classification by enlarging interclass distances and reducing intraclass variations in the feature space. Experimental results demonstrate state-of-the-art performance, with the model achieving 99.34% accuracy for benign\/malignant classification and 95.97% accuracy for eight-subtype classification on the BreakHis dataset, confirming its effectiveness in breast cancer histopathology analysis.<\/jats:p>","DOI":"10.3390\/info16060444","type":"journal-article","created":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T11:12:57Z","timestamp":1748344377000},"page":"444","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Focal Cosine-Enhanced EfficientNetB0: A Novel Approach to Classifying Breast Histopathological Images"],"prefix":"10.3390","volume":"16","author":[{"given":"Min","family":"Liu","sequence":"first","affiliation":[{"name":"School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuzhen","family":"Pei","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minghu","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.canlet.2019.12.007","article-title":"Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges","volume":"471","author":"Huang","year":"2020","journal-title":"Cancer Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"12","DOI":"10.3322\/caac.21820","article-title":"Cancer statistics 2024","volume":"74","author":"Siegel","year":"2024","journal-title":"CA A Cancer J. 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