Skin Diseases Psoriasis Detection Using Machine Learning
Year: 2026
Type: article
Abstract: The proposed Skin Disease Psoriasis Detection System using Machine Learning is designed as a modular web-based application to ensure structured development, scalability, and efficient execution of tasks. The system is divided into five main modules: User Module, Admin Module, Image Processing Module, Machine Learning Module, and Database Module. Each module performs a specific function that collectively contributes to accurate and automated disease detection.The User Module handles registration, authentication, image upload, result viewing, and profile management, enabling seamless interaction between users and the system. The Admin Module provides secure access for system monitoring, user management, dataset handling, and overall control of application activities. The Image Processing Module enhances input images through resizing, normalization, noise removal, and optional augmentation to improve model accuracy. The Machine Learning Module acts as the core component, where a Convolutional Neural Network (CNN) is trained, validated, and used for predicting whether the uploaded skin image is affected by psoriasis or not. Finally, the Database Module ensures secure storage and efficient retrieval of user data, images, and prediction results.
Authors: Thakare, Samiksha Vijay, Sahu, Amit
Institution: G H Raisoni University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7162019585
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