Research on Pigmented Skin Disease Recognition based on Deep Learning
Due to the interference of factors such as hair and texture in dermoscopy images,the i-dentification of pigmented skin diseases often leads to misclassification.To enhance the accuracy of pigmented skin disease recognition,reduce model parameters,and decrease computational complexity,a novel approach based on MobileViT is proposed.The MobileViT model is utilized as the foundation for training,leveraging transfer learning techniques.Further improvements are made by fusing the output of the MobileViT block with the CBMA attention mechanism and applying knowledge distillation using EfficicntNctv2-xl.Experimental results demonstrate that the en-hanced algorithm achieves a 7.28％increase in recognition accuracy compared to the original model,a-long with reduced computational complexity and parameter volume.Moreover,an interface for classif-ying and identifying nine types of pigmented skin diseases has been developed,providing an experi-mental basis for research on the medical-assisted diagnosis of pigmented skin diseases.
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