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Benign and Malignant Oral Lesion Image Classification Using Fine-Tuned Transfer Learning Techniques

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dc.contributor.author Islam, Md. Monirul
dc.contributor.author Alam, K. M. Rafiqul
dc.contributor.author Uddin, Jia
dc.contributor.author Ashraf, Imran
dc.contributor.author Samad, Md Abdus
dc.date.accessioned 2024-05-04T06:21:15Z
dc.date.available 2024-05-04T06:21:15Z
dc.date.issued 2023-11-01
dc.identifier.issn 2075-4418
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/12215
dc.description.abstract Oral lesions are a prevalent manifestation of oral disease, and the timely identification of oral lesions is imperative for effective intervention. Fortunately, deep learning algorithms have shown great potential for automated lesion detection. The primary aim of this study was to employ deep learning-based image classification algorithms to identify oral lesions. We used three deep learning models, namely VGG19, DeIT, and MobileNet, to assess the efficacy of various categorization methods. To evaluate the accuracy and reliability of the models, we employed a dataset consisting of oral pictures encompassing two distinct categories: benign and malignant lesions. The experimental findings indicate that VGG19 and MobileNet attained an almost perfect accuracy rate of 100%, while DeIT achieved a slightly lower accuracy rate of 98.73%. The results of this study indicate that deep learning algorithms for picture classification demonstrate a high level of effectiveness in detecting oral lesions by achieving 100% for VGG19 and MobileNet and 98.73% for DeIT. Specifically, the VGG19 and MobileNet models exhibit notable suitability for this particular task. en_US
dc.language.iso en_US en_US
dc.publisher MDPI Publications en_US
dc.subject Image segmentation en_US
dc.subject Transfer learning en_US
dc.subject Techniques en_US
dc.subject Malignant en_US
dc.title Benign and Malignant Oral Lesion Image Classification Using Fine-Tuned Transfer Learning Techniques en_US
dc.type Article en_US


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