基于CNN与Transformer混合的轻量化皮肤病变分类网络OA

Existing classification algorithms often suffer from a large number of parameters,high computational complexity,and sub-optimal classification performance in classifying skin lesion images,due to their uneven distribution attibutes.To address this issue,in this paper,we propose a lightweight transformer module and a novel strategy that combines Convolutional Neural Network(CNN)with Transformer to enhance network classification performance.Additionally,we adopt an inverse class loss function weighting scheme to mitigate the impact of imbalanced image category distribution during training.The lightweight transformer ex-tracts essential features from input sequences,and performs separable self-attention computations to capture global feature informa-tion from skin lesion regions.This approach addresses the computational limitations of traditional transformer.Furthermore,our new strategy effectively integrates shallow global detail features with deep semantic features,enhancing the network's expressive ability.Experimental results on the HAM10000 dataset demonstrate that our algorithm outperforms other comparative methods in terms of evaluation metrics.Remarkably,we achieve these results while maintaining a model size of only 2.3 million parameters,which holds significant promise for advancing automatic skin lesion classification tools.