Real-Time Dermatological Classification and Knowledge Retrieval: A Deep Learning and Web Scraping Approach

In this paper, we present a cutting-edge AI-classin skin diseases discovering system with the capability for dynamic knowledge retrieval. By means of deep learning techniques, the system uses pretrained models DenseNet121 and VGG19, Xception as well as a brand new model for an accurate classification from dermatological images to 9 categories of skin diseases. This dataset is then processed with data augmentation and preprocessing to make the model performance better. In addition to classification, the system also has an Access-Augmented Generation (RAG) module which will O-Genization disease-specific documentation for richer context. Finally, real time descriptions are retrieved from Wikipedia corresponding to the reported disease using an informal information retrieval mechanism. The process consists of: forming structured URLs (in compliance with web policies) and using custom user-agent headers to respect our examination of the policies implemented by BeautifulSoup with data pars. The derived textual data is concisely represented in simple, user-readable form for easier usability. All the pipeline is packaged in a Flask-based web interface for easy end-user interaction. This proof-of-concept, which integrates image classification, runtime knowledge augmentation and web integration highlights its potential for the significant benefits to dermatological diagnostics and improved medical insights.