Ayush Tripathi

https://doi.org/10.64823/ijter.2604036

The increasing prevalence of skin diseases and the shortage of dermatologists in many regions have created a need for automated and efficient diagnostic systems. Traditional diagnosis relies heavily on expert analysis, which may be time- consuming and inaccessible in remote areas. This paper proposes a Skin Disease Prediction System that utilizes machine learning and deep learning techniques to detect and classify skin diseases from images. The system employs a convolutional neural network (CNN) model trained on a large dataset of skin disease images. It integrates a full-stack architecture consisting of a frontend interface for user interaction, a backend server for processing, and a database for storing patient records and predictions. The system ensures accurate classification, fast processing, and scalability. Experimental results demonstrate that the proposed system achieves high accuracy and reliability in predicting multiple skin diseases. This solution can assist dermatologists, improve early diagnosis, and enhance healthcare accessibility.