Deep Learning-Based Skin Diseases Classification using Smartphones

Abstract

Skin disease recognition is one of the essential topics in the medical industry. Detecting skin disease from appearance can be difficult due to the similar appearance of skin lesions. In some cases, such as the monkeypox virus, the illness must be quickly determined, and the patients must be isolated to reduce the spreading of the disease. This study aims to create a deep learning-based automated intelligent mobile application to detect skin disease. First, different small-size pretrained networks are trained for skin lesion image classification. Then, the most suitable network from the viewpoint of both performance and mobile compatibility is transformed into the TensorFlow Lite format. Finally, a mobile application is created on the Android platform that utilizes the smartphone's camera to obtain images and uses TensorFlow Lite to make predictions. The proposed system produces 74.27% classification accuracy for seven classes on a combined dataset. It produces comparable/better results compared to the literature. Owing to the proposed system, the patients can make a preliminary diagnosis of their lesions using their smartphones. Thus, risky patients can be encouraged to visit the hospital for a definitive diagnosis. In addition, the mobile application can avoid undue stress and false alarms. Automated skin lesion analysis systems can assist people in ensuring rapid diagnosis. Convolutional neural networks (CNNs) can be used to realize these types of systems. Integrating CNNs into smartphones opens new doors for creating mobile intelligent systems. Mobile devices can be utilized to assist people in many different aspects, such as healthcare, with the help of machine learning.image & COPY; 2023 WILEY-VCH GmbH