Enhanced Deep Learning Approaches for Classifying Skin Disorders

Enhanced Deep Learning Approaches for Classifying Skin Disorders M. Tanmaya, Dr.N.NeelimaPriyanka, V.Mahesh Reddy, J.Hemanth, Sk.Rumana 06 03 The goal of this project is to create a comprehensiveand reliable system that is capable of properly diagnosing a widerange of skin illnesses This aim is what drives this researchLeveraging a huge and diverse dataset supplied from Kaggle which encompasses a comprehensive collection of photos depict ingvarious dermatological disorders like as Acne Melanoma Psoriasisand many more the initiative leverages stateofthe art deeplearning algorithmsThrough the skillful use of Convolutional Neural NetworksCNNs wellknown VGG Visual Geometry Group networks and ResNet Residual Networks architectures the project in tends to attain levels of precision in illness detection that have never been obtained before Through the use of these cutting edge models the system attempts to painstakingly evaluate and categorize photos ofskin diseases As a result dermatologists are provided with essential information about the diagnosis of diseases and theplanning of treatmentsThe ultimate objective of this attempt is to supply der matologistswith a categorization tool that is both automatic and dependablewhich will complement their experience and enhance theirdiagnostic capabilities The goal of the system is to transform the area of dermatology by enabling improved efficiency accuracy and efficacy in disease detection and patient treatment This will be accomplished by integrating modern deep learning technologies intoclinical practice in a seamless manner

2026