Machine Learning Techniques for the detection of Various Skin Diseases

Most of the algorithms used in deep learning and machine learning have contributed to increased diagnostic accuracy and treatment efficacy for skin diseases, a prevalent health issue. The research methodology mainly provides an area for machine learning for skin condition detection, with an emphasis on prominent machine learning algorithms. The current difficulties and constraints were evaluated, and some remedies were proposed. The main motive is to make a model which will help us in categorizing eight types of skin disorders. In the first stage, we use several data preprocessing techniques like scaling and normalization. Then, in the subsequent stages, we are using Mobilenetv2, Resnet50 for classifying eight types of skin illnesses and calculate their accuracy, positive predictive value, balanced F-Measure, sensitivity. This study outlines a visual analysis approach for skin condition assessment, involving the capture of digital images of affected skin regions and utilizing computational techniques to identify the specific disorder.