{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T05:03:50Z","timestamp":1779685430027,"version":"3.53.1"},"reference-count":77,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T00:00:00Z","timestamp":1779667200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Physiol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Accurate classification of colonoscopic images is essential for early detection and characterization of colorectal diseases. Recent advances in deep learning, particularly transformer-based architectures and graph neural networks (GNNs), provide alternative strategies for modeling global contextual information and relational structures in image representations. This study evaluates transformer-based and graph-based frameworks under a unified experimental protocol for endoscopic colon disease classification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Experiments were conducted on the Kvasir V2 dataset using two primary paradigms: (i) a Vision Transformer (ViT) with selective fine-tuning and learning-rate scheduling, and (ii) a CNN\u2013GNN pipeline integrating image embeddings with graph construction strategies (cosine similarity, k-nearest neighbors, and epsilon-radius graphs) and multiple GNN architectures. Performance was evaluated using accuracy, precision, recall, and macro-F1 score, with Grad-CAM used for qualitative interpretability analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The selectively fine-tuned Vision Transformer achieved 94.6% accuracy with a macro-F1 score of 0.94. The best graph-based configuration (ViT embeddings with epsilon graph and GIN aggregation) achieved 92% accuracy and 0.92 macro-F1 score.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Transformer-based contextual modeling provides strong discriminative capability for image-level colon disease classification, while graph-based relational modeling offers competitive performance when paired with high-quality embeddings.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fphys.2026.1734299","type":"journal-article","created":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T04:41:07Z","timestamp":1779684067000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Grad-CAM based deep learning analytics for image-level colon disease classification based on graph neural networks and vision transformers"],"prefix":"10.3389","volume":"17","author":[{"given":"Chaohui","family":"Zhen","sequence":"first","affiliation":[{"name":"Department of Gastrointestinal Surgery, The First Dongguan Affiliated Hospital, Guangdong Medical University","place":["Dongguan, Guangdong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Canhua","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Surgery, The First Dongguan Affiliated Hospital, Guangdong Medical University","place":["Dongguan, Guangdong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Song","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Gastrointestinal Surgery, The First Dongguan Affiliated Hospital, Guangdong Medical University","place":["Dongguan, Guangdong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zihong","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Gastrointestinal Surgery, The First Dongguan Affiliated Hospital, Guangdong Medical University","place":["Dongguan, Guangdong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Umar","family":"Muhammad Ibrahim","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Automation, Huazhong University of Science and Technology","place":["Wuhan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kavimbi","family":"Chipusu","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Division of Biomedical Engineering, University of Saskatchewan","place":["Saskatoon, SK, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Biao","family":"Zheng","sequence":"additional","affiliation":[{"name":"Department of Surgery, The First Dongguan Affiliated Hospital, Guangdong Medical University","place":["Dongguan, Guangdong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Gastrointestinal Surgery, The First Dongguan Affiliated Hospital, Guangdong Medical University","place":["Dongguan, Guangdong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,25]]},"reference":[{"key":"B29","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5528622","article-title":"Boosting breast cancer detection using convolutional neural network","volume":"2021","author":"Alanazi","year":"2021","journal-title":"J. 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