Skin Disease Detection for Kids at School Using Deep Learning Techniques

Abstract

Due to the rapid spread of skin diseases among children in school, and the fact that skin disease is the most common contagious disease spreading within students in school, this study investigates the factors that could help in early detection of these skin diseases using AI techniques. The texture and color of the skin can change as a result of the disease. Examples of these diseases are chickenpox, impetigo, scabies, infectious erythema, skin warts, and other infectious skin diseases. Skin disorders are long-term and contagious, it can be detected early and with high accuracy before it become a long-term problem. This research builds a system of skin disease detection using the CNN technique and a pre-trained VGG19 model. In addition, the dataset contains 4500 images that were collected from different sources to train the VGG19 model. Data augmentation technique such as zooming, cropping, and rotating were used. After that, the Adamax optimizer, which is most suitable for the proposed methodology, was used to obtain high accuracy and required results. This study achieved a high accuracy of 99% compared to other similar researchs. It can be concluded that this system is very reliable which can be integrated to smart schools as part of IOT systems.