A Skin Cancer Classification Method Based on Discrete Wavelet Down-Sampling Feature Reconstruction

Abstract

Aiming at the problems of feature information loss during down-sampling, insufficient characterization ability and low utilization of channel information in skin cancer diagnosis of melanoma, a skin pathological mirror classification method based on discrete wavelet down-sampling feature reconstruction is proposed in this paper. The wavelet down-sampling method is given first, and the multichannel attention mechanism is introduced to realize the pathological feature reconstruction of high-frequency and low-frequency components, which reduces the loss of pathological feature information due to down-sampling and effectively utilizes the channel information. A skin cancer classification model is given, using a combination of depth-separable convolution and 3×3 standard convolution and wavelet down-sampling as the input backbone of the model to ensure the perceptual field while reducing the number of parameters; the residual module of the model is optimized using wavelet down-sampling and Hard-Swish activation function to enhance the feature representation capability of the model. The network weight parameters are initialized on ImageNet using transfer learning and then debugged on the augmentation HAM10000 dataset. The experimental results show that the accuracy of the proposed method for skin cancer pathological mirror classification is significantly improved, reaching 95.84%. Compared with the existing skin cancer classification methods, the proposed method not only has higher classification accuracy but also accelerates the classification speed and enhances the noise immunity. The method proposed in this paper provides a new classification method for skin cancer classification and has some practical value.