{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T09:08:50Z","timestamp":1788858530389,"version":"build-2803163510"},"reference-count":23,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T00:00:00Z","timestamp":1788825600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific Research Projects Coordination Unit of Firat University","award":["MF.26.28"],"award-info":[{"award-number":["MF.26.28"]}]},{"award":["MF.26.28"],"award-info":[{"award-number":["MF.26.28"]}],"id":[{"id":"https:\/\/ror.org\/05teb7b63","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Oral squamous cell carcinoma (OSCC) has high mortality rates and leads to serious health problems when diagnosed late. This situation is considered a public health problem. Histopathological examination, which is an important point in the diagnosis of the disease, is a time-consuming and manual process that requires expertise. Methods: This study presents a deep learning-based approach for the automated classification of normal oral epithelium and oral squamous cell carcinoma (OSCC) from histopathological images. The performance of state-of-the-art architectures such as Vision Transformer (ViT), CLIP, ConvNextV2, Deit, and Dinov2 was comparatively analyzed. Based on the results, a hybrid architecture combining the strengths of the models is proposed. Results: In experiments conducted on a dataset of 696 histopathological images, the ConvNextV2 and ViTL16 architectures stood out among the basic models with accuracy rates around 84%. However, the most significant contribution of this study is the proposed method, which combines the global context capability of Transformer-based models with the local feature extraction power of CNN-based models using the Efficient Channel Attention (ECA)\u2014Gated Features model. This hybrid model, created by integrating the ViTL16, Dinov2, and ConvNextV2 architectures, achieved 89.95% accuracy, 89.82% F1 score, and 89.95% sensitivity with a KNN classifier, outperforming the baseline models in the literature. Conclusions: The results obtained demonstrate that the fusion of multiple architectures increases diagnostic reliability in medical image analysis and can assist pathologists as a decision support mechanism.<\/jats:p>","DOI":"10.3390\/diagnostics16182885","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T08:10:22Z","timestamp":1788855022000},"page":"2885","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Classification of Oral Squamous Cell Carcinoma from Histopathological Images Using a Hybrid Deep Learning Model"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4891-7873","authenticated-orcid":false,"given":"Furkan","family":"Talo","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Firat University, 23119 Elazig, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8005-7386","authenticated-orcid":false,"given":"Ahmet Bedri","family":"Ozer","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Firat University, 23119 Elazig, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"18","DOI":"10.4103\/jomfp.JOMFP_113_17","article-title":"Oral cancer statistics in India on the basis of first report of 29 population-based cancer registries","volume":"22","author":"Sharma","year":"2018","journal-title":"J. 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