Skin Disease Detection and Classification

Abstract: Skin diseases affect millions of people worldwide, ranging from common conditions like acne and eczema to severe cases such as melanoma and carcinoma. Early and accurate diagnosis is crucial for effective treatment and improved patient outcomes. However, traditional diagnostic methods rely heavily on clinical expertise, which can be subjective and time-consuming. This research focuses on developing an automated skin disease detection and classification system using image processing and machine learning techniques. The proposed approach utilizes Convolutional Neural Networks (CNNs) to analyze dermatological images and classify different skin conditions, including melanoma, psoriasis, eczema, and fungal infections. A large dataset of labeled skin images is used to train the deep learning model, enabling it to recognize subtle patterns and variations in skin texture, color, and morphology. Techniques such as data augmentation, transfer learning, and feature extraction enhance the model’s accuracy and generalization ability. Experimental results demonstrate that the proposed system achieves high classification accuracy compared to traditional machine learning models. The automation of skin disease detection reduces human error, minimizes diagnosis time, and enables remote diagnosis through telemedicine applications. The integration of artificial intelligence in dermatology holds great potential in aiding dermatologists, especially in regions with limited access to specialized healthcare services.