Abstract
BACKGROUND: This study aimed to classify dysplastic and healthy oral epithelial histopathological images, according to WHO and binary grading systems, using the Vision Transformer (ViT) deep learning algorithm-a state-of-the-art Artificial Intelligence (AI) approach and compare it with established Convolutional Neural Network models (VGG16 and ConvNet). METHODS: A total of 218 histopathological slide images were collected from the Department of Oral and Maxillofacial Pathology at Tehran University of Medical Sciences archive and combined with two online databases. Two oral pathologists independently labeled the images based on the 2022 World Health Organization (WHO) grading system (mild, moderate and severe), the binary grading system (low risk and high risk), including an additional normal tissue class. After preprocessing, the images were fed to the ViT, VGG16 and ConvNet models. RESULTS: Image preprocessing yielded 2,545 low-risk, 2,054 high-risk, 726 mild, 831 moderate, 449 severe, and 937 normal tissue patches. The proposed ViT model outperformed both CNNs with the accuracy of 94% (VGG16:86% and ConvNet: 88%) in 3-class scenario and 97% (VGG16:79% and ConvNet: 88%) in 4-class scenario. CONCLUSIONS: The ViT model successfully classified oral epithelial dysplastic tissues with a high accuracy, paving the way for AI to serve as an adjunct or independent tool alongside oral and maxillofacial pathologists for detecting and grading oral epithelial dysplasia.