Tinea-Corporis Skin Disease Detection Using CNN and Kernel SVM

Skin Diseases are growing more prevalent in today's society for a variety of causes. The WHO estimates that 1 billion people around the world suffer from skin conditions, ranging from uncomplicated acne to potentially fatal skin cancer. Tinea Corporis known as Ringworm, is a fungal infection on the skin and it is detected by our proposed model. Ringworm is a common skin infection that affects approximately 20-25% of the world's population at some point in their lives, and also according to the report of Indian Council of Medical Research Ringworm is on the rise in India with cases increasing from 2% to 40% in some regions of the country. This research paper presents the image acquisition methods, datasets, deep learning architectures, and frameworks used in skin disease diagnosis. The proposed methods have shown promising results in accurately identifying skin diseases and have the potential to improve the efficiency and reliability of diagnosis. This paper presents a hybrid method that uses machine learning algorithms for clinical features and convolutional neural network for acne images. However, rigorous and valid testing is essential to ensure their effectiveness and safety. Performance was evaluated on the basis of accuracy, precision, and recall and finally results are compared.