@article{article_1601385, title={ScabAI: A Deep Learning-Based Mobile Application for Scabies Detection from Skin Images}, journal={Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji}, volume={13}, year={2025}, DOI={10.29109/gujsc.1601385}, author={Yılmaz, Hakan and Can, Zeynep Nida and Baki, Hatice Şevval and Çökmez, Tahsin and Özdem, Mehmet}, keywords={Uyuz, derin öğrenme, CNN, mobil arayüz, erken tanı}, abstract={Scabies, a contagious skin disease caused by the Sarcoptes scabiei mite, remains a significant public health concern globally. This study aims to develop a mobile application, ScabAI, which uses a deep learning model based on Convolutional Neural Networks (CNNs) to detect scabies from skin images. The model was trained using a dataset of 500 images, divided equally between scabies and non-scabies cases, and achieved high performance metrics, including 96.7% accuracy, 96% sensitivity, 97.3% specificity, and a 96.5% F1 score. These results demonstrate the model’s reliability and effectiveness in detecting scabies, outperforming many existing models. The mobile application allows users to capture or upload images of suspected scabies lesions, providing rapid and accurate preliminary diagnoses. ScabAI offers a practical, user-friendly tool that can be beneficial for both healthcare providers and individuals, supporting early detection, timely treatment, and reducing the risk of disease transmission. This study underscores the potential of integrating artificial intelligence with mobile platforms for improved dermatological care, particularly in resource-limited settings. Future research should focus on expanding the dataset to enhance generalization and exploring additional AI techniques to refine detection accuracy. ScabAI not only contributes to AI-assisted dermatology but also serves as a scalable model for developing similar tools targeting other skin conditions. This innovative approach addresses both clinical needs and user accessibility, advancing healthcare outcomes and public health initiatives.}, number={4}, publisher={Gazi Üniversitesi}