Abstract: Skin diseases are among the most common health concerns worldwide, yet early diagnosis and access to dermatological care remain limited, especially in remote and underserved regions. This project presents an intelligent, mobile-friendly skin health diagnosis application designed to provide quick and accessible preliminary analysis of common skin conditions. The system leverages artificial intelligence and computer vision techniques to analyse user-provided skin images and symptoms, enabling automated detection and classification of various dermatological issues. The application is built using Python-based frameworks and integrates deep learning models trained on dermatological image datasets to ensure reliable prediction accuracy. In addition to image-based diagnosis, the system provides symptom-based suggestions, precautionary measures, and basic remedies to guide users toward informed healthcare decisions. The mobile responsive design ensures seamless accessibility across smartphones and tablets, enhancing usability and real-time interaction. Furthermore, the platform incorporates a secure user interface, fast processing, and scalable deployment using lightweight web technologies, making it suitable for both personal and clinical assistance. By combining AI-driven diagnosis with a mobile friendly interface, the proposed system aims to bridge the gap between technology and accessible skincare awareness. Future enhancements may include integration with telemedicine services, real-time dermatologist consultation, multilingual support, and continuous model improvement using larger medical datasets.

Keywords: Skin Disease Detection, Mobile Health (mHealth), Deep Learning, Convolutional Neural Network (CNN), Mobile Net, Image Processing, Telemedicine, AI-Based Diagnosis Net, Image Processing, Telemedicine, AI-Based Diagnosis.

Downloads: | DOI: 10.17148/IARJSET.2026.13342