SKINDX: A Machine Learning Approach to Skin Disease Identification

Skin diseases are among the most common health conditions affecting individuals across different age groups and regions. In many resource-constrained areas, access to dermatological specialists is limited, leading to delayed diagnosis and treatment. SKINDX is a Python-based intelligent skin disease detection system that leverages image processing and machine learning techniques to automatically classify common dermatological conditions such as eczema, psoriasis, acne, and related skin disorders. The system integrates OpenCV for image preprocessing and feature extraction, and utilizes Weka or Deeplearning4j for classification modeling. Users can upload an image of the affected skin area, after which the system processes the image, extracts features, and predicts the most probable disease along with a confidence score. A structured database stores image records, predictions, and associated medical information such as symptoms and treatment recommendations. The proposed framework also includes secure authentication, report generation, automated document handling, and scalable backend architecture. SKINDX aims to assist healthcare professionals and patients by providing preliminary diagnostic insights, especially in underserved reg