Abstract: The biggest organ of the body is the human skin. Its weight lies between six and nine pounds and its surface area is about two square yards. The inner part of the body is separated by the skin from the outer environment. Melanoma is a type of cancer that mostly starts in pigment cells (melanocytes) in the skin. To improve the diagnostic performance of melanoma, a dermoscopy technique was developed. Dermoscopy is a non-invasive skin imaging technique of acquiring a magnified and illuminated image of a region of skin for increased clarity of the spots on the skin. Dermatological diseases are the most prevalent diseases worldwide. Despite being common, its diagnosis is extremely difficult and requires extensive experience in the domain. Melanoma is the deadliest form of skin cancer. While curable with early detection, only highly-trained specialists are capable of accurately recognizing the disease. As expertise is in limited supply, automated systems capable of identifying disease could save lives, reduce unnecessary biopsies, and reduce costs. We use a dual-stage approach that effectively combines Computer Vision on clinically evaluated histopathological attributes to accurately identify the disease. In the first stage, the image of the skin disease is subject to various kinds of pre-processing techniques followed by feature extraction. The second stage involves the use of algorithms to identify diseases based on the histopathological attributes observed on analyzing the skin.
Keywords: Image Recognition, Skin Diseases, Melanoma, Dermoscopy Images, Classification Learner App.
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