Non-Invasive Skin Disease Diagnostic System Using Deep Learning

Human skin is an important part of the body that needs attention. Skin diseases are quite common in a country like India with varied climate and high pollution levels. The usual diagnosis of skin diseases is based on visual examination and certain tests performed by a dermatologist. But this is a time-consuming process and can also end up in the wrong diagnosis. Skin disease diagnosis has undergone a revolution because of the significant development in the field of machine learning, notably convolutional neural networks (CNN). CNNs have the potential to completely change the way skin illnesses are diagnosed, tracked, and treated. This study investigates the application of various machine learning techniques in skin problem identification. By using CNN analysis of skin disease images, identification of patterns and making accurate predictions can be done with an unprecedented accuracy and consistency. In addition to diagnosis, machine learning models can treat different skins individually according to their type, thus simplifying the doctor's role. This study aims to use CNN to improve dermatology practices by introducing efficiency and accuracy. This study includes a comprehensive literature review examining existing CNN models and performance metrics. Goals include collecting health data, using CNN's predefined models, comparing their performance, recommending the best models for diagnosing skin diseases, and improving the user interface. It presents a benchmark-based neural network model that may contribute to advances in dermatology in identifying skin diseases through machine learning using data from HAM10000 and DERMNET. The effectiveness of the model is determined by comparing it with other existing models by evaluating quality indicators such as accuracy, precision, and F1 score.