Detection of Melanoma Using Distinct Features

Abstract

Abstract: Melanoma is one of the most dangerous types of skin cancer because it grows fast and causes the majority of skin cancer fatalities. Hence, early detection is a very important task to treat melanoma. In this work, we suggest a method for dermoscopic images that segments skin lesions based on the U-Net architecture with VGG-16 encoder and the semantic segmentation. Base on the segmented skin lesion, diagnostic imaging systems can evaluate skin lesion features to classify them. The suggested approach requires fewer resources for training, and it is suitable for computing systems without powerful GPUs, but the training accuracy is still high enough (above 95 %). In the experiments, we train the model on the ISIC dataset–a common dermoscopic image dataset. To assess the performance of the proposed skin lesion segmentation method, we evaluate the Sorensen-Dice and the Jaccard scores and compare to other deep learning-based skin lesion segmentation methods. Experimental results showed that skin lesion segmentation qualities of the proposed method are better than ones of the compared methods.