A Skin Disease Classification Model Based on DenseNet and ConvNeXt Fusion

Skin disease is one of the most common diseases. Due to the intricate categories of skin diseases, their symptoms being very similar in the early stage, and the lesion samples being extremely unbalanced, their classification is challenging. At the same time, under the conditions of limited data, the generalization ability of a single reliable convolutional neural network model is weak, the feature extraction ability is insufficient, and the classification accuracy is low. Therefore, in this paper, we proposed a convolutional neural network model for skin disease classification based on model fusion. Through model fusion, deep and shallow feature fusion, and the introduction of an attention module, the feature extraction capacity of the model was strengthened. In addition, a series of works such as model pre-training, data augmentation, and parameter fine-tuning were conducted to upgrade the classification performance of the model. The experimental results showed that when working on our private dataset dominated by acne-like skin diseases, our proposed model outperformed the two baseline models of DenseNet201 and ConvNeXt_L by 4.42% and 3.66%, respectively. On the public HAM10000 dataset, the accuracy and f1-score of the proposed model were 95.29% and 89.99%, respectively, which also achieved good results compared with other state-of-the-art models.