Skin diseases classification method based on SE-Inception-v4 convolutional neural network

Skin diseases not only endanger physical health but also cause psychological problems. Traditional manual diagnosis has strong subjectivity and limitations. Recently, the use of computer-aided diagnosis technology based on deep convolutional neural networks to classify and recognize dermatological images has been widely used. In order to further improve the classification effect, we propose a method to merge the SENet network with the Inception-v4 network. By comparing the DensenNet-121, VGG-16, and ResNet-101 networks, the effectiveness of the SE-Inception-v4 network is verified, and the SENet network has also verified the effectiveness of model performance improvement. Experimental results show that the improved deep learning algorithm in this paper can improve the accuracy of skin disease image classification and has certain guiding significance for the research and application of computer-aided diagnosis in the medical field.