Melanoma Classification in Dermoscopy Images via Ensemble Learning on Deep Neural Network

Jie Song, Jiawei Li, Shiqiang Ma, Jijun Tang, Fei Guo

Abstract: Auotmatic melanoma classification in dermoscopy images is a very important task, which can help improve diagnostic accuracy and reduce mortality. Deep convolutional neural network (DCNN) has developed rapidly in recent years, but it is still a challenging task due to the intra-class variation and inter-class similarity of melanoma. We proposed a novel neural network integration model, which is composed of three parts: First, we use U-net segmentation network to generate masks and use the masks to crop original images; Second, we use five state-of-the-art DCNNs to extract features of cropped images, and add the squeeze-excitation block (SE block) to emphasize useful features; Finally, we construct a new neural network with local connection to integrate the classification results, extract features of different class of results, and integrate the results of each class separately. Local connection can integrate each class separately, maximizing the advantages of different networks in various classes. We evaluate our model on ISIC 2017 challenge dataset, and the result shows that our method has better performance compared with the existing methods.