Automated Skin Lesion Segmentation using VGG-UNet

Skin cancer is a serious worldwide health worry with high mortality rates and high grimness. For this reason, to successfully diagnose skin lesions, a computer-aided automatic diagnostic system is required. One of the most crucial methods to do that is the segmentation of skin lesions. In this paper, we present a new model that integrates two architectures, the U-Net and the VGG19. Furthermore, to improve the results of segmentation, we also employ image preprocessing, including the Dull-Razor algorithm for hair removal and Contrast Limited Adaptive Histogram Equalization (CLAHE) to improve the image contrast. Moreover, we evaluated our model on three datasets: ISIC 2016, ISIC 2017, and ISIC 2018. Our suggested model achieved satisfactory results compared to the state-of-the-art.