Automated Skin Disease Analysis and Detection using AI Powered Mobile Application

Dr.V.Seedha Devi; Mahalakshimi P.V, Anitha  A

doi:10.15662/IJRAI.2026.0903004

Authors

Dr.V.Seedha Devi Associate Professor, Department of Information Technology, Jaya Engineering College, Anna University, Chennai, Tamil Nadu, India Author
Mahalakshimi P.V, Anitha A UG Student, Department of Information Technology, Jaya Engineering College, Anna University, Chennai, Tamil Nadu, India Author

DOI:

https://doi.org/10.15662/IJRAI.2026.0903004

Keywords:

Machine learning, python, Flutter, Firebase

Abstract

Skin diseases are one of the most common health problems affecting people worldwide, and early identification is essential for effective treatment and prevention. However, due to lack of awareness, limited access to dermatologists, and time constraints, many individuals fail to seek timely medical attention. To overcome these challenges, this project presents an AI-based system for multi skin disease detection using Flutter and Firebase, aimed at providing a preliminary and accessible skin disease identification solution. The proposed system utilizes image processing and traditional machine learning techniques to analyze skin images and detect multiple skin diseases without using deep learning models. The user captures or uploads a skin image through a mobile application. The image undergoes preprocessing steps such as resizing, noise removal, and color normalization to improve image quality. Feature extraction techniques are then applied to obtain important visual characteristics related to skin color, texture, and shape. These extracted features are classified using machine learning algorithms to identify possible skin diseases. The mobile application is developed using Flutter, which enables cross-platform compatibility and a user friendly interface. Firebase is used as the backend service to handle user authentication, cloud storage, and secure data management. The detected skin disease, along with relevant confidence information, is displayed to the user, and the results are securely stored in Firebase for future reference and analysis.

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