A Novel Approach for Psoriasis Disease Detection using Folder Division and Image Augmentation

Abstract

Psoriasis is a serious autoimmune skin disorder. It appears up on the skin as red, scaly regions. This effect on people lives who impacted with this disease. Early and accurate detection is more important for recovering from this disease. This method provides a new way to detect psoriasis disease. It utilises image augmentation and folder division techniques for improving the performance of deep learning models. The image augmentation techniques like as rotation, scaling, flipping, and color shifting were utilised on a set of psoriasis images. This avoided the models from refitting and improved the variety of the dataset. The folder division technique helped to organize training, validation, and testing datasets. This structure improves the deep learning model's applicability to different psoriasis symptoms. The results show that combining image augmentation with folder division techniques improves the accuracy and early detection of psoriasis. Dermatologists can use this as a useful tool for diagnosing skin problems