Skin Disease Detection based on Machine Learning Techniques

Skin is the human body's exterior integument. Human skin pigmentation varies from person to person, and skin types include dry, oily, and mixed. The human skin's diversity offers bacteria and other microbes with a diverse home. Melanocytes in the human skin create melanin, which can absorb harmful UV radiation from the sun, causing skin damage and cancer. In most third-world societies, the requisite technologies for early identification of many diseases are still unavailable. The technique of image segmentation aids in the diagnosis of various skin disorders. The goal of this research is to use image processing techniques to diagnose the skin illness from a given image set. Deblurring and noise reduction were performed on the captured image set before it was processed. If acne, dermatomyositis, candidiasis, cellulitis, Scleroderma, chickenpox, ringworm, eczema, psoriasis, Melonama, and other skin illnesses are left untreated in their early stages, they can lead to a variety of health consequences and even death. The technique of images egmentation aids in the diagnosis of various kind is orders. They have taken two classes here. To classify the condition, there are two types of skin: normal and abnormal. Melonama and Acnephotos will be processed in the system in the abnormal condition.