Multi-Label Dental Image Classification via Vision Transformer for Orthopantomography X-ray Images

Dental disease is extensively taken as one of the most important healthcare issues globally. Early detection of dental diseases using imagery-based data can significantly improve clinical diagnosis and treatment, hence decreasing the risk of serious health ailments. To address the task of dental image classification, a large number of deep learning models, especially convolutional neural networks, have been presented and achieved promising outcomes in a variety of benchmarks, including dealing with dental X-ray images in clinical practices. Nevertheless, the aforementioned models lack acceptable adaptability and stability when applied to practical scenarios. A major drawback of convolutional neural networks is their limited ability to capture the global relationships between distant pixels in medical pictures due to their small receptive field. This paper presents a new vision transformer model to address this disparity, specifically focusing on the task of dental picture classification. The proposed method utilizes the multi-head self-attention module while excluding the convolution operators. Furthermore, the process of transfer learning is utilized to optimize the weighting parameters of the visual transformer being presented. The experimental results provide evidence that the suggested method outperforms the current cutting-edge deep learning techniques. © 2024 U-turn Press LLC.