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Oral squamous cell carcinoma is often detected at advanced stages, leading to poor patient outcomes, and traditional diagnostic methods can be subjective and physically demanding for pathologists. By comparing different Artificial intelligence models, including machine learning and deep learning, this study aims to create a more standardized and rapid diagnostic aid that can support pathologists and increase patient survival rates.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 115 cases (49 OSCC, 66 normal) were captured at 100x and 400x magnifications. Twelve Gray Level Co-occurrence Matrix (GLCM) features were extracted. Machine learning models, including Fine Tree, Medium Tree, Linear Discriminant, Linear Support Vector Machine, K-Nearest Neighbors, and Neural Network (MLP) model were developed. Model performance was evaluated across four distinct classification tasks, assessing the contribution of histopathological images alone and in fusion with demographic\/clinical data. Statistical validation involved 5-fold cross-validation on the training set and independent testing, reporting sensitivity, specificity, precision, F1 score, and overall accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Analysis of Gray Level Co-occurrence Matrix (GLCM) features revealed significant textural disparities between normal and OSCC. Specifically, OSCC images showed trends of finer texture (lower autocorrelation), higher local intensity variation, increased complexity (entropy), greater intensity disparities (dissimilarity), and reduced uniformity (lower energy, maximum probability, and homogeneity) compared to normal counterparts. Leveraging these distinct textural signatures, the developed machine learning models exhibited exceptional classification capabilities, frequently achieving 100% sensitivity, specificity, precision, F1 score, and overall accuracy across all tasks during both validation and testing of models. This high performance highlights the reliability of the proposed approach for differentiating OSCC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study demonstrated that statistical image features effectively capture distinct textural differences between normal and OSCC tissues. The developed machine learning models showed outstanding classification performance, demonstrating their strong potential for accurate, automated OSCC diagnosis. This near-perfect accuracy could revolutionize diagnostic workflows, minimizing subjective interpretations and enhancing overall efficiency.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12903-025-07482-1","type":"journal-article","created":{"date-parts":[[2025,12,27]],"date-time":"2025-12-27T03:04:45Z","timestamp":1766804685000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Texture-based image feature analysis for the classification of oral squamous cell carcinoma using machine learning approach"],"prefix":"10.1186","volume":"26","author":[{"given":"Salma","family":"Bahaa","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omneya","family":"Ramadan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeinab Elsayed","family":"Darwish","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eslam","family":"EL-Fiky","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mai M.","family":"Saleh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,27]]},"reference":[{"issue":"1","key":"7482_CR1","doi-asserted-by":"publisher","first-page":"3664408","DOI":"10.1155\/adph\/3664408","volume":"2024","author":"VR Umapathy","year":"2024","unstructured":"Umapathy VR, Natarajan PM, Swamikannu B, Jaganathan S, Rajinikanth S, Periyasamy V. 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Accessed 23 May 2025."}],"container-title":["BMC Oral Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12903-025-07482-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12903-025-07482-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12903-025-07482-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T15:50:07Z","timestamp":1781625007000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12903-025-07482-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,27]]},"references-count":49,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["7482"],"URL":"https:\/\/doi.org\/10.1186\/s12903-025-07482-1","relation":{},"ISSN":["1472-6831"],"issn-type":[{"value":"1472-6831","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,27]]},"assertion":[{"value":"16 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Our study utilized retrospective tissue samples from the archive of Oral Pathology Department at Alexandria University. Informed consents for the use of Patients\u2019 data and material in research were obtained from all patients before their initial surgery, as part of the standard institutional procedure in Cranio Maxillo-facial and Plastic Surgery Department in Alexandria main hospital. Additionally, this retrospective study protocol was reviewed and approved by Research Ethics Committee, Faculty of Dentistry, Alexandria University, Institutional Review Board (IRB) under approval number (001056- IORG0008839). All procedures performed in our study involving human data and material were in accordance with the Declaration of Helsinki.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not Applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"105"}}