Skin Disease Prediction

Abstract

Machine Learning (ML) is an emerging technology and a discipline to study how to use the machine to simulate human learning activities. It focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. Prediction in ML involves using a trained model to make predictions on forecasts, about new unseen data based on patterns learned from past data. On the contrary healthcare plays an important role in human life. Healthcare presents a range of complex and challenging problems that require sophisticated solutions. For example, accurately diagnosing diseases based on medical images can be difficult, but ML models can be trained to identify subtle patterns that might be missed by human experts. Skin diseases are very common nowadays and spreading widely among people in present time. Therefore, development of data mining techniques can efficiently distinguish classes of skin disease is important. Due to lack of medical facilities available in the remote areas, patients usually ignore early symptoms which may worsen the situation as time progresses. Hence, there is a rising need for automatic skin disease detection system with high accuracy. Therefore, an application is developed which uses Convolutional Neural Network (CNN) as it is the most efficient algorithm for image classification. Then a machine learning model is built for better accuracy. Then Softmax is used for multi classification. Deep Learning is used to train the model, Deep Learning is a part of Machine Learning in which unlike Machine Learning it uses large dataset and hence the number of classifiers is reduced substantially. The final model will be able to identify and classify the type of disease which has affected a particular area of skin.