A Novel Vision Neural Network for Pan-Cancer Classification by Constructing Somatic Mutation Map With Feature Selection Optimization

Due to the high fatality rate of cancer, timely detection and treatment in the early stage of cancer is very important. In this study, a method for constructing gene mutation maps based on the principle of RGB three-channel image was proposed to realize the dimensional transformation of somatic mutation data, making it suitable for the current image classification model. In order to better capture the global features of the mutation map, this paper proposes a network model M-MNet based on the inverted residual module and the multi-head self-attention module, which can effectively extract both local and global features, and the classification effect is better than that of the existing network models.