A Vision Transformer Model with Hyperparameter Optimization for Oral Cancer Image Classification

Oral cancer is a significant public health concern and is among the most common malignant tumors of the head and neck. Its incidence and mortality rates remain persistently high, especially in regions where smoking and betel nut chewing are prevalent. Due to its high mortality rate, early detection is crucial for improving patient outcomes. However, early symptoms of oral cancer often resemble benign oral lesions, leading to delayed diagnosis. In this study, a vision transformer (ViT) model with Optuna (ViTOPT) is employed to perform classification tasks of identifying oral cancer images. The Optuna is used to determine hyperparameters in ViT. Histological images are obtained from a publicly available dataset. Three classification tasks with histological images namely classifying oral squamous cell carcinoma (OSCC) and leukoplakia (LEUK), classifying the presence of dysplasia, and classifying OSCC and leukoplakia with or without dysplasia are performed in this study. Numerical results reveal that the proposed ViTOPT framework is able to provide satisfactory performance in oral cancer recognition. Thus, the proposed ViTOPT model is a feasible and effective alternative in identifying oral cancer.