Medical image classification based on enhanced Vision Transformer

Abstract

With the rapid development of artificial intelligence, its related skills have been widely used in medical treatment. Medical image classification has become an indispensable and increasingly important part of disease diagnosis and treatment to allow accurate and rapid treatment. According to the existing neural network failed to extract local and global features significantly at the same time, this paper uses Gamma transform and the combined model of CNN and Visual Transformer to classify the images of chest x-ray patients. Our model uses convolution operation and self-attention mechanism to enhance representational learning. It adopts a parallel structure to retain local features and global features to the greatest extent. The results showed that our model has a better classification effect than Vision Transformer, which shows its availability and great potential in medical image assisted diagnosis.