Machine Learning Based Skin Disease Analyzer with Image Processing

People may acquire skin illnesses as a consequence of hereditary factors, the natural ageing process, hormones, allergic responses, exposure to the sun or hazardous chemicals, and other environmental variables. Skin disorders are among the most prevalent and pervasive diseases in the world. The diagnosis is exceedingly challenging despite the fact that this condition is rather common since there are slight changes in skin tone, colour, and the presence of hair. When it is in its earlier phases, individuals sometimes fail to recognise the impacts of skin diseases. At the moment, dermatologists diagnose skin conditions by performing a sample, which is subsequently analysed, and then manually give drugs to patients. In this research, we offer a technique for the diagnosis of skin diseases that is based on image processing, Python, and the Yolov3 tool. Our goal is to eliminate the need for physical examination and to deliver reliable findings in a fast way. An picture of the portion of the patient's skin that is infected may be sent as an input to the system by the patient. On this picture, several image processing methods are used, and feature values are retrieved and located, in addition to the illness that is under investigation. The suggested approach is particularly helpful in geographical locations with a limited number of dermatologists. Acne, melanoma, blisters, and cold sores are the four forms of skin diseases that are discussed in this article, along with a technique for diagnosing each of them. The fundamental objective of this approach is to attain the greatest degree of accuracy in the prediction of skin diseases as is possibly conceivable.