A Decision Machine Learning Support System for Human Skin Disease Classifier

Abstract

For the past decades, the prevalence of dermatological disorders, especially human skin diseases, has been rising. The majority of these diseases are contagious and are also based on visual perceptions. Although many works have shown promising results on the image classification problem, only a few studies compare traditional machine learning models and the recent deep learning models with various metrics on human skin diseases classification. Therefore, in this paper, we propose A Decision Machine Learning Support System for Human Skin Disease Classifier (DSSC) to classify five skin disease classes, including 750 images gained from the Dermnet dataset. In particular, we perform image pre-processing, image resizing, image interpolation, and image augmentation to adjust the input images into the proper format for all models. Through the extensive experiments, RestNet50 outperforms other deep learning and traditional machine learning methods in all metrics, including accuracy, precision, recall, and F-measure by a large margin.