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Endocrinol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Accurate detection of gastrointestinal diseases from endoscopic images remains challenging due to substantial inter-class similarity, intra-class variability, and heterogeneous lesion presentation. While convolutional neural networks (CNNs) effectively capture fine mucosal textures, their limited receptive field restricts global contextual reasoning. Conversely, transformer architectures model long-range dependencies but may underrepresent localized structural detail.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study proposes a dual-branch hybrid transformer framework that integrates global token-level reasoning from ViT-L\/32 with hierarchical spatial modeling from MaxViT-L. An adaptive gated fusion mechanism is introduced to dynamically regulate the relative contribution of local and global representations on a per-sample basis, enabling content-aware cross-scale integration. The model was evaluated on a multi-class gastrointestinal endoscopic dataset using comprehensive quantitative metrics, class-wise analysis, ablation studies, confidence interval estimation, and statistical validation. Post-hoc explainability was further investigated using multiple complementary XAI techniques.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The proposed framework demonstrated consistently high accuracy, sensitivity, specificity, and area under the ROC curve across eight gastrointestinal disease categories, outperforming several state-of-the-art transformer and hybrid architectures. The XAI analyses further confirmed lesion-focused activation consistency and stable decision-making.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The findings indicate that adaptive local\u2013global feature integration through gated fusion enhances both diagnostic performance and interpretability, supporting the potential of hybrid transformer architectures for reliable computer-aided gastrointestinal disease classification.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1869015","type":"journal-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T04:21:36Z","timestamp":1784002896000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A hybrid ViT-L\/32\u2013MaxViT-L architecture with adaptive gated fusion for multiclass gastrointestinal disease detection and multi-method post-hoc explainability"],"prefix":"10.3389","volume":"17","author":[{"given":"Shahid Mohammad","family":"Ganie","sequence":"first","affiliation":[{"name":"Department of Health Information Management and Technology, College of Applied Medical Sciences, King Faisal University","place":["Al-Ahsa, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pijush Kanti","family":"Dutta Pramanik","sequence":"additional","affiliation":[{"name":"School of Computer Applications and Technology Galgotias University Greater Noida","place":["Uttar Pradesh, India"]},{"name":"McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston","place":["Houston, TX, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongming","family":"Zhao","sequence":"additional","affiliation":[{"name":"McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston","place":["Houston, TX, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1109\/access.2024.3483432","article-title":"AI in endoscopic gastrointestinal diagnosis: A systematic review of deep learning and machine learning techniques","volume":"12","author":"Lewis","year":"2024","journal-title":"IEEE Access"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2944676","article-title":"Review on the applications of deep learning in the analysis of gastrointestinal endoscopy images","volume":"7","author":"Du","year":"2019","journal-title":"IEEE Access"},{"key":"B3","doi-asserted-by":"publisher","first-page":"598","DOI":"10.1016\/j.vgie.2020.08.013","article-title":"Artificial intelligence in gastrointestinal endoscopy","volume":"5","author":"Pannala","year":"2020","journal-title":"VideoGIE"},{"key":"B4","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-031-21014-3_39","article-title":"Contextual attention network: Transformer meets U-Net","volume-title":"Machine Learning in Medical Imaging (Mlmi 2022). 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