A framework for skin disease analyzer.

Skin diseases are among the most common and widespread diseases in the world, with people developing them as a result of inheritance, ageing, hormones, allergic reactions, sun or toxic chemical exposure, and environmental factors. Despite its prevalence, diagnosis is extremely difficult due to subtle differences in skin tone, hue, and hair presence. Many people ignore the effects of skin disease when it is in its early stages. Skin diseases are currently diagnosed using a biopsy procedure, which is then examined, and medications are administered manually by physicians. To avoid manual inspection and provide accurate results in a timely manner, this paper proposes a skin disease detection method based on image processing, Python, and the Yolov3 tool. The patient can provide an image of the infected skin area as an input to the system. On this image, image processing techniques are used, and feature values are extracted and detected, as well as the disease being analyzed. The proposed system is especially useful in areas where dermatologists are scarce. The paper describes a method for identifying four types of skin disease: acne, melanoma, blisters, and cold sores. The primary goal of this method is to achieve the highest possible level of skin disease prediction accuracy. [ABSTRACT FROM AUTHOR]