Research on Dermoscopic Segmentation based on Multi-scale Convolutional Neural Network

The skin is an important organ of the human body, and the major skin diseases represented by melanoma are difficult to find and difficult to treat, which directly threaten the patient’s life safety. Therefore, rapid and accurate dermatoscopy diagnosis[1] is particularly important. The lack of experienced dermatologists limits the large-scale early diagnosis or screening of melanoma. Based on the above problems, this paper uses the method of multi-scale convolutional neural network to segment the Dermoscopic image by means of image segmentation in artificial intelligence to help medical personnel to achieve reliable diagnostic reference. In the overall segmentation network, this paper first enhances the contrast of the original image through preprocessing, and uses data enhancement and segmentation to increase the dataset. In the model training phase, this paper adopts the basic segmentation framework of U-Net[2] network. Different from U-Net networks, this paper introduces multi-scale features and multi-scale fusion mechanisms to deeply mine and fuse dermoscopic image features. Secondly, the model uses Focal Loss as the fundus segmentation loss function, and finds the optimal parameters of the loss function through a random grid search algorithm. In this paper, for the problem of positive and negative sample imbalance in dermoscopic images, this paper proposes an adaptive Focal Loss loss function and Channel-Wise Attention mechanism to adaptively weight the feature maps.