SkinAI: deep learning for skin disease classification

Lau, Jian Yi (2026) SkinAI: deep learning for skin disease classification. Final Year Project, UTAR.

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Abstract

Skin diseases are among the most prevalent health concerns worldwide, affecting millions annually and posing challenges to timely diagnosis and effective treatment. Access to dermatological expertise remains unevenly distributed, particularly in underserved and rural areas, where delays in diagnosis can lead to advanced complications and increased healthcare costs. Leveraging advancements in artificial intelligence (AI) and deep learning, this project introduces "SkinAI", a mobile-based application for skin disease classification. This study highlights the importance of developing an AI-powered solution that bridges the gap between technology and real-world application. By integrating a deep learning-based model into a user-friendly mobile application, "SkinAI" empowers individuals and healthcare professionals with an accessible diagnostic tool for early skin disease detection. The proposed system aims to classify common skin conditions using a robust dataset, ensuring stable accuracy and adaptability across diverse skin types and diseases, including eczema, psoriasis, basal cell carcinoma, and viral infections. The contributions of this project extend to advancing AI in medical imaging, enhancing healthcare accessibility, and promoting cost-effective diagnostic solutions. "SkinAI" not only facilitates rapid skin condition analysis but also provides users with intuitive interaction, enabling non-technical users to leverage cutting-edge AI technologies effortlessly.

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