With the advancement of computer technology,the existing Transformer has been expanded into a network structure for processing computer vision tasks.In order to improve the early diagnosis rate of melanoma and the cure rate of skin disease patients,this paper proposes an improved network model based on PiT (pyramid pooling transformer) to realize automatic classification of dermoscopic images of seven skin lesions.The model of this paper is mainly composed of the PiT module and the anti-interference module.Pit inherits the advantages of ViT and uses pooling to perform spatial size conversion to improve the robustness of the model.The pre-trained PiT network has a large number of natural image features,and the PiT part of the network can provide the required image features for downstream classification tasks.In this paper,an anti-interference module is designed to resist the influence of interference factors (such as hair and foreign object occlusion) in the dermoscopic image,thereby improving the performance of the model.Improve classification accuracy.Experimental results show that the classification accuracy,precision,recall,and F1-score values of this model on the ISIC 2018 verification set are as high as 91.58%,83.59%,89.92%,86.34%,and the number of frames per second (FPS) reaches 85 Hz.Compared with several existing advanced classification networks,the classification performance and model efficiency have been improved,and it has relative advantages,which proves that the model in this paper has certain practical value.