A Machine Learning Framework for Skin Disease Classification and Care Recommendation

Abstract

A Machine Learning Framework for Skin Disease Classification and Care Recommendation are among the most common medical issues worldwide, impacting millions across various age groups. Early and accurate detection is essential for effective treatment and management. However, in many remote and underserved regions, access to dermatologists is limited, leading to delayed diagnosis and care. To address this challenge, the proposed system to automatically classify skin diseases based on image analysis. The project integrates image classification with real-time patient support by offering health suggestions and displaying relevant dermatologist contact information post prediction. Using publicly available datasets and the ISIC Archive, the system applies preprocessing techniques such as resizing, normalization, and augmentation to improve model performance. The model, developed using transfer learning with architectures, accurately identifies common skin conditions including eczema, psoriasis, and melanoma. Upon classification, the system presents the user with basic treatment advice and a list of nearby specialists. This web-based interface, built, ensures accessibility, scalability, and user-friendliness. By bridging the gap between AI diagnosis and real-world medical action, this project aims to provide a practical, AI-powered dermatological assistant, especially beneficial in regions lacking adequate healthcare infrastructure.