Dermoscopy image classification based on StyleGANs and decision fusion

Skin cancer is one of the most common cancers in humans in recent years, affecting people of all ages. If skin cancer is treated in time, the curative effect and prognosis are favorable. At present, dermoscopy is an effective way for the early diagnosis of skin cancer. However, manual detection is highly dependent on the clinical experience of doctors, and the complexity of the dermoscopy image itself poses a great challenge to the classification. Therefore, we propose a decision fusion method. Through transfer learning, based on multiple pre-trained convolutional neural networks (CNNs), we use the block to combine multiple CNNs and finally make decisions through multiple blocks. The method of decision fusion can solve the generalization capability of a individual convolutional neural network (CNN) model, and is more robust and stable than the traditional fusion strategy. Based on ISIC 2019 dataset, we use StyleGANs to generate high-quality images to alleviate the problem of less and uneven distribution of the dermoscopy image dataset and improve the classification effect of CNNs. Our proposed method can improve the accuracy of dermoscopy image classification and provide help for dermatologists.