Dermoscopic Image Classification Method Based on FL-ResNet50

In this paper, a classification method for seven types of dermoscopic images based on deep convolution neural network model is proposed. The training set is amplified using the data augmentation method. For the multiclassification of dermoscopic images, the multiclassification model (FL-ResNet50 model) based on ResNet50 model and multiclassification Focal Loss function is proposed. The experimental results show that the micromean F-1 value of FL-ResNet50 model is 0.88, which is better than the results obtained using the traditional ResNet50 model. The proposed method realizes seven types of dermoscopic image classification and forms a complete and continuous system model by image preprocessing, feature extraction, and prediction model learning. The FL-ResNet50 model improves upon the classification performance and efficiency of the previous models and has important application value.