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

Md Monirul Islam; K M Rafiqul Alam; Jia Uddin; Imran Ashraf; Md Abdus Samad

doi:10.3390/diagnostics13213360

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

Diagnostics (Basel). 2023 Nov 1;13(21):3360. doi: 10.3390/diagnostics13213360.

Authors

Md Monirul Islam¹, K M Rafiqul Alam², Jia Uddin³, Imran Ashraf⁴, Md Abdus Samad⁴

Affiliations

¹ Department of Software Engineering, Daffodil International University, Daffodil Smart City (DSC), Birulia, Savar, Dhaka 1216, Bangladesh.
² Department of Statistics, Jahangirnagar University, Dhaka 1342, Bangladesh.
³ AI and Big Data Department, Endicott College, Woosong University, Daejeon 34606, Republic of Korea.
⁴ Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of Korea.

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.

Keywords: MobileNet; VGG19; benign; data-efficient image transformer (DeiT); malignant; oral lesions; transfer learning.

Grants and funding

This research is funded by Woosong University Academic Research 2023.