Skin Disease Identification Using Deep Learning Techniques

Skin diseases are a common global health issue, especially in rural areas with limited access to dermatological care. Traditional diagnostic methods are often time-consuming and rely heavily on expert interpretation. This project proposes an automated skin disease detection system using Convolutional Neural Networks (CNNs) for accurate and efficient diagnosis. The system uses image processing techniques to analyze skin images and identify patterns associated with various diseases. A deep learning model is trained using labeled datasets and implemented with Keras. The final model is deployed through a Flask web application, offering a user-friendly interface for real-time predictions. Users can upload an image of the affected area and receive instant classification results. The system improves accessibility and reduces dependency on specialists for initial screening. It also enables early detection, which is crucial for effective treatment. The accuracy of the model enhances trust in automated systems. This approach supports the growing need for AI in healthcare, especially in under-resourced areas. Index Terms: Skin Disease Detection, Convolutional Neural Networks (CNN), Deep Learning, Image Processing, Keras, Flask, Medical Image Classification, Artificial Intelligence in Healthcare, Automated Diagnosis, Dermatology, Web Application.