Artificial intelligence—based diagnosis of oral leukoplakia using deep convolutional neural networks Xception and MobileNet-v2
ObjectiveThe 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.Materials and methodsCli...
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Oral Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/froh.2025.1414524/full |
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| author | Elakya Ramesh Anuradha Ganesan Krithika Chandrasekar Lakshmi Prabhu Manickam Natarajan |
| author_facet | Elakya Ramesh Anuradha Ganesan Krithika Chandrasekar Lakshmi Prabhu Manickam Natarajan |
| author_sort | Elakya Ramesh |
| collection | DOAJ |
| description | ObjectiveThe 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.Materials and methodsClinical 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.ResultsCNN 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.ConclusionWe demonstrate that CNN models are capable of 89%–92% accuracy and can be best used to discern OL and its clinical types from other white lesions of the oral cavity. |
| format | Article |
| id | doaj-art-5407eab6dfc1431ea9dcd37a2e1b64f6 |
| institution | OA Journals |
| issn | 2673-4842 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Oral Health |
| spelling | doaj-art-5407eab6dfc1431ea9dcd37a2e1b64f62025-08-20T01:49:14ZengFrontiers Media S.A.Frontiers in Oral Health2673-48422025-03-01610.3389/froh.2025.14145241414524Artificial intelligence—based diagnosis of oral leukoplakia using deep convolutional neural networks Xception and MobileNet-v2Elakya Ramesh0Anuradha Ganesan1Krithika Chandrasekar Lakshmi2Prabhu Manickam Natarajan3Department of Oral Medicine and Radiology, SRM Dental College, Chennai, Tamil Nadu, IndiaDepartment of Oral Medicine and Radiology, SRM Dental College, Chennai, Tamil Nadu, IndiaDepartment of Oral Medicine and Radiology, SRM Dental College, Chennai, Tamil Nadu, IndiaDepartment of Clinical Sciences, Center of Medical and Bio-Allied Health Sciences and Research, College of Dentistry, Ajman University, Ajman, United Arab EmiratesObjectiveThe 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.Materials and methodsClinical 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.ResultsCNN 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.ConclusionWe demonstrate that CNN models are capable of 89%–92% accuracy and can be best used to discern OL and its clinical types from other white lesions of the oral cavity.https://www.frontiersin.org/articles/10.3389/froh.2025.1414524/fulloral premalignant disorderoral leukoplakiaconvolutional neural networksartificial intelligencedeep learningdiagnostic accuracy |
| spellingShingle | Elakya Ramesh Anuradha Ganesan Krithika Chandrasekar Lakshmi Prabhu Manickam Natarajan Artificial intelligence—based diagnosis of oral leukoplakia using deep convolutional neural networks Xception and MobileNet-v2 Frontiers in Oral Health oral premalignant disorder oral leukoplakia convolutional neural networks artificial intelligence deep learning diagnostic accuracy |
| title | Artificial intelligence—based diagnosis of oral leukoplakia using deep convolutional neural networks Xception and MobileNet-v2 |
| title_full | Artificial intelligence—based diagnosis of oral leukoplakia using deep convolutional neural networks Xception and MobileNet-v2 |
| title_fullStr | Artificial intelligence—based diagnosis of oral leukoplakia using deep convolutional neural networks Xception and MobileNet-v2 |
| title_full_unstemmed | Artificial intelligence—based diagnosis of oral leukoplakia using deep convolutional neural networks Xception and MobileNet-v2 |
| title_short | Artificial intelligence—based diagnosis of oral leukoplakia using deep convolutional neural networks Xception and MobileNet-v2 |
| title_sort | artificial intelligence based diagnosis of oral leukoplakia using deep convolutional neural networks xception and mobilenet v2 |
| topic | oral premalignant disorder oral leukoplakia convolutional neural networks artificial intelligence deep learning diagnostic accuracy |
| url | https://www.frontiersin.org/articles/10.3389/froh.2025.1414524/full |
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