{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,2]],"date-time":"2026-11-02T08:45:59Z","timestamp":1793609159378,"version":"4.5.0"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T00:00:00Z","timestamp":1742515200000},"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. Oral Health"],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>The present study aims to employ and compare the artificial intelligence (AI) convolutional neural networks (CNN) Xception and MobileNet-v2 for the diagnosis of Oral leukoplakia (OL) and to differentiate its clinical types from other white lesions of the oral cavity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>Clinical photographs of oral leukoplakia and non-oral leukoplakia lesions were gathered from the SRM Dental College archives. An aggregate of 659 clinical photos, based on convenience sampling were included from the archive in the dataset. Around 202 pictures were of oral leukoplakia while 457 were other white lesions. Lesions considered in the differential diagnosis of oral leukoplakia like frictional keratosis, oral candidiasis, oral lichen planus, lichenoid reactions, mucosal burns, pouch keratosis, and oral carcinoma were included under the other white lesions subset. A total of 261 images constituting the test sample, were arbitrarily selected from the collected dataset, whilst the remaining images served as training and validation datasets. The training dataset were engaged in data augmentation to enhance the quantity and variation. Performance metrics of accuracy, precision, recall, and f1_score were incorporated for the CNN model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>CNN models both Xception and MobileNetV2 were able to diagnose OL and other white lesions using photographs. In terms of F1-score and overall accuracy, the MobilenetV2 model performed noticeably better than the other model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We demonstrate that CNN models are capable of 89%\u201392% accuracy and can be best used to discern OL and its clinical types from other white lesions of the oral cavity.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/froh.2025.1414524","type":"journal-article","created":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T06:37:36Z","timestamp":1742539056000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["Artificial intelligence\u2014based diagnosis of oral leukoplakia using deep convolutional neural networks Xception and 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