{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T03:53:52Z","timestamp":1769658832517,"version":"3.49.0"},"reference-count":48,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T00:00:00Z","timestamp":1769558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>The automated and accurate results of classifying histopathology images are necessary in the early detection of cancer, especially the common cancers such as Colorectal Cancer (CRC) and Lung Cancer (LC). Nonetheless, classical deep learning frameworks often face challenges because the intra-class variations are large, the relations across classes are alike, and the quality of images is not stable. In order to eliminate these constraints, a multi-layer diagnostic framework is offered in detail. This process starts with a strong preprocessing pipeline, which involves gamma correction, bilateral filtering, and adaptive CLAHE, resulting in statistically significant changes in image quality quantitative measures. The hybrid attention architecture is presented and includes an Xception backbone, a Convolutional Block Attention Module (CBAM), a Transformer block, and an MLP classifier to successfully combine local features with global context. The proposed model achieved an outstanding performance with a classification of 99.98%, 99.58%, and 99.33% percent on LC25000, CRC-VAL-HE-7K, and NCT-CRC-HE-100K when tested on three publicly available datasets. In order to enhance transparency, very detailed explainability analyses are conducted with the help of layer-wise feature visualization and Grad-CAM. Finally, the real-world example of this framework is presented by its implementation in a web-based platform, which can be a useful and easy-to-use tool in helping to diagnose a pathology.<\/jats:p>","DOI":"10.3390\/make8020031","type":"journal-article","created":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T15:56:12Z","timestamp":1769615772000},"page":"31","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Attention-Driven Feature Extraction for XAI in Histopathology Leveraging a Hybrid Xception Architecture for Multi-Cancer Diagnosis"],"prefix":"10.3390","volume":"8","author":[{"given":"Shirin","family":"Shila","sequence":"first","affiliation":[{"name":"Department of Food Technology and Nutritional Science, Mawlana Bhashani Science and Technology University, Tangail 1902, Bangladesh"}]},{"given":"Md. Safayat","family":"Hossain","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail 1902, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7276-3766","authenticated-orcid":false,"given":"Md Fuyad Al","family":"Masud","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, North Dakota State University, Fargo, ND 58102, USA"}]},{"given":"Mohammad Badrul Alam","family":"Miah","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail 1902, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0963-5419","authenticated-orcid":false,"given":"Afrig","family":"Aminuddin","sequence":"additional","affiliation":[{"name":"Department of Information System, Universitas Amikom, Yogyakarta 55283, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9172-5212","authenticated-orcid":false,"given":"Zia","family":"Muhammad","sequence":"additional","affiliation":[{"name":"Department of Computing, Design, and Communication, University of Jamestown, Jamestown, ND 58405, USA"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,28]]},"reference":[{"key":"ref_1","first-page":"209","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA A Cancer J. 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