Classification of Skin Disease using CNN

The majority of people around the world suffer from skin conditions at some point in their lives. These conditions need to be identified and treated as soon as possible in order to prevent complications and improve the outcome of the patient. Skin diseases can be difficult to diagnose, even for dermatologists who are experts in the field. In order to identify and classify skin diseases, a computer algorithm known as a Convolutional Neural Network (CNN) can be used. Using CNN, artificial intelligence is able to learn from images and make predictions based on the information it gathers from them. In order to train CNN to classify skin conditions accurately, a large number of images of skin diseases can be used to train the CNN. This paper describes a method for classifying skin diseases using CNNs. As part of the training process, the system was taught a large collection of thermoscopic images, which show the distribution of heat patterns on the skin. As a result of this study, CNN was able to successfully classify different skin diseases with an accuracy of 90% in terms of validation, and 95% in terms of classification. In addition to dermatologists and primary care physicians, healthcare professionals can also use this system to make an accurate diagnosis of skin diseases by using this system. Furthermore, patients can use it as a means of identifying and understanding their skin conditions as well.