Published Paper Details:

Skin diseases are among the most common health concerns worldwide, affecting millions of people every year. Early detection plays a crucial role in preventing complications and ensuring timely treatment. This project, titled "Enhancing Healthcare Decision: Image Processing Techniques for Detection of Skin Diseases," aims to build a web-based system for automated skin disease classification using Convolutional Neural Networks (CNN). The front-end of the system is developed using React JS, providing users with an intuitive interface to upload skin lesion images. Flask is used as the backend framework to handle API requests and communicate with the CNN model, which processes the images and predicts the likelihood of various skin diseases. The model is trained on a large dataset of skin lesion images and utilizes advanced image processing techniques to enhance prediction accuracy. Our system aims to assist healthcare professionals by providing quick and reliable diagnostics, reducing the burden on clinical resources. This project demonstrates the effectiveness of deep learning in medical applications and highlights the potential of integrating machine learning models with modern web technologies.