MONKEYPOX SKIN LESION

Abstract: Early detection of infectious skin diseases such as Monkeypox, Chickenpox, and Measles is essential for preventing complications and reducing the spread of infection. However, access to dermatologists and advanced diagnostic facilities is limited in many regions, making timely diagnosis difficult. This project presents an intelligent web-based Skin Disease Detection System that uses deep learning techniques to automatically classify skin images into Monkeypox, Chickenpox, Measles, or Normal categories. The system employs a Convolutional Neural Network (CNN) model trained on skin image datasets to perform accurate image-based classification. Users can upload skin images through a web interface, and the system processes the input to generate prediction results along with confidence scores and basic medical information. A secure database is used to store user details and prediction history, while automated email notifications are sent to users for report confirmation. An admin or doctor dashboard is included for monitoring system activity and analysing disease trends. Experimental evaluation shows that the system provides reliable predictions, improves accessibility to preliminary diagnosis, and supports early disease awareness, making it a useful decision-support tool for healthcare applications.

Keywords: Skin Disease Detection, Deep Learning, Convolutional Neural Network, Medical Image Analysis, Web- Based Healthcare System, Automated Diagnosis