Human Skin Disease Detection and Classification Using Ensemble Learning

Skin disorders are thought to be common in humans and carry several invisible risks, including the potential to cause psychological sadness, lower self-esteem, and, in more serious cases, skin cancer. Medical professionals must diagnose these skin conditions, but doing so requires highly sophisticated diagnostic tools because they have trouble seeing clearly while examining images of the conditions. This paper focuses on skin disease detection and classification using ensemble learning. This is done using Multiple Skin Disease Detection and Classification data sets from the ISIC Archive through employing the bagging, boosting, and stacking methodologies for better diagnosis. To compare the proposed ensemble of CNN models and individual CNN models, the observations were made. This strategy is useful for dermatologists to identify skin diseases at an early stage or at the first instance to prevent further deterioration of the skin health of their patients.