A Novel Deep Convolutional Neural Network for Diagnosis of Skin Disease

Abstract

Due to its intricacy, dermatology presents the most challenging and uncertain terrain for diagnosis. Skin conditions like Carcinoma and Melanoma are frequently very challenging to identify in the early stages and are much more challenging to define independently. The use of pattern recognition models to automate detection has been studied by a number of writers. This research describes a novel Deep Convolutional Neural Network (DCNN) for Skin Disease Detection. The photographs of skin would undergo processing to remove unwanted noise as well as to improve the photos. The performance of classification will be greatly impacted by the pixel values of a picture. The picture is classified using the softmax classifier method by feature extraction utilising DCCN, and a diagnosis report is produced as the result. In comparison to more classic approaches like KNN (K-Nearest Neighbour) and CNN, this methodology will provide results faster and with improved accuracy, precision, and recall. With a detection time of 10,000 milliseconds, DCNN achieved accuracy, precision, and recall percentages of 98.4%, 96.3%, and 97.2%, respectively.