Analyzing Skin Disease Using XCNN (eXtended Convolutional Neural Network)

Abstract

Skin disease is one of the major concerns for clinicians and researchers. Fungus, germs, allergies, and viruses are the main causes of skin diseases. There has always been unsaid competition between conventional and advanced computing-based techniques, and with these new techniques, cost of treatment is also being reduced drastically. In this paper, a deep learning-based model named eXtended Convolutional Neural Network (XCNN) has been proposed to classify three types of skin diseases (i.e., acne, rosacea, and melanoma). XCNN is easy-to-use, economic, and accurate. It will help clinicians to identify and categorize such diseases at the initial stage through automated screening. The proposed work is designed for multi-classification that takes digital images and applies XCNN to identify the type of disease. The model has been built on the dataset of the various skin disease images. It gives 95.67% accuracy in recognizing the diseases with improved recall, f1-score, and precision values compared to other state-of-the-art models.