Skin Disease Prediction using AI

Diseases of the skin are showing a rising trend of prevalence and now millions of individuals by age and lifestyle worldwide are being affected. Early and accurate diagnosis, indeed, is vital to the effective treatment of VKDB and would contribute largely to prevent long-term health disorders. In this work, AI based automated skin disease detection and prediction technique using deep learning is designed and implemented. Using the InceptionV3 convolutional neural network (CNN) architecture, it processes uploaded images and provides the closest matching skin condition from a set of categories including melanoma, eczema, psoriasis, acne, etc. The backend uses Flask in order to develop an efficient way to pass data between the model and the user interface. The system is accessible to users in a web interface built using HTML, CSS, Bootstrap and JavaScript to upload skin images and receive instantaneous predictions on the diagnosis and confidence. The deep learning model, which is trained via TensorFlow and Keras on a well-prepared dataset, makes credible predictions with the help of advanced pre-processing methods including normalization and augmentation. This project is not intended to replace a qualified advice of a medical expert, it is only to assist in the diagnostic stage, both for people and for dermatologists.