Diagnostic Analysis of Skin Disease Using Deep Learning

Skin diseases are common disease mostly ignored but sometimes it becomes dangerous for human being. Ignoring this is not a good sign, but most of the time it's normal to see any skin disease on human body, and its color shows the depth of the seriousness. So it is clear that the image of the skin is the core factor of diagnosis of the disease related with it. Technologies of image processing based on machine learning and ANN provide optimum approaches to find the skin disease which will help in diagnosis of the disease. Over decades of research, edge detection techniques have evolved from simple gradient-based methods to sophisticated deep learning approaches. In this paper edge detection techniques are used to diagnosis of skin disease by finding the color difference of skin from neighboring body cell and comparing it by using machine learning algorithmsand the performances are evaluated on HAM10000 skin cancer dataset. Different machine learning algorithms are used for finding the best image analysis tool for skin disease detection in medical field.