Surveying the Field of Dermatological Disease Detection: Machine Learning and Image Analysis Perspectives

Due to the complexity of the condition and the lengthy diagnostic process, skin diseases are a widespread health issue. In addition to having an impact on one's health, skin conditions can also have a negative psychological and social impact if not identified and treated at an early stage. Most of them are brought on by infections or by different environmental factors. Eczema, psoriasis, melanoma, acne cherry, vitiligo, basal cell carcinoma, squamous cell carcinoma, melanocytic nevi, seborrheic keratosis, and acrochordon (skin tags) are a few examples of distinct skin diseases. However, the expense of such a diagnostic is still quite high. The advancement of image processing methods and machine learning has enabled a swift and precise diagnosis, contributing to the early identification of skin conditions. Skin-related problems affect millions of people worldwide. This research gives a literature review on the many methods used to identify skin diseases. In this study, we scrutinized six papers that presented various methods for skin disease identification. These approaches encompassed techniques such as extracting features utilizing the gray level co-occurrence matrix (GLCM) and employing a multi-layer perceptron classifier (MLP) to differentiate between melanocytic nevi and malignant melanoma, among others. The opportunities for improving the diagnosis and treatment of skin ailments, assisting in the earlier detection of skin cancer, and improving patient outcomes differ based on the suggested process.