Digital Image Processing for Identification of Types of Skin Diseases Using the Convolutional Neural Network (CNN) Method - Ria, Sona Nova; Walid , Miftahul; Umam, Busro Akramul (Universitas Panca Marga, 2022)
Skin disease is the most common disease and the fastest to infect the human body. This happens because the skin is the first organ to receive stimulation from the outside in the form of touch, temperature and other stimuli. Skin disease consists of several types that have a color texture that is almost the same by naked eye. Thus, an approach is needed to recognize the types of skin diseases with the help of image processing systems and artificial neural networks. The identification method used in this study is the Convolutional Neural Network (CNN). The infected skin image is used as an input image for image processing. Prior to identification, image pre-processing was carried out, namely resizing, grayscaling, using the Convolutional Neural Network method. The testing process in this study used 70 types of skin disease images for validation data and 35 types of skin disease images for data testing. The results of this study the Convolutional Neural Network method can recognize each image of a type of skin disease with anaccuracy of 98% in the validation testing process and 85% in the testing process.