A deep learning based approach for automated skin disease detection using Fast R-CNN

Skin conditions vary widely in terms of its symptoms and criticality which can be persistent or temporary, pain-free or painful, mild or severe and at times situational or genetic in nature. This varying complexity and uncertainty not only make it difficult for a patient to sense it, but also becomes a daunting task for doctors to deal with it. Consequently, if remained ignored or untreated, it can even be fatal at times. Therefore, the need for a rapid detection system for skin disorder is a must to reduce its criticality level. This paper is an attempt to develop a system using deep learning technology to detect skin diseases accurately. Using the Fast R-CNN architecture of deep learning, appropriate annotation technique and proper selection of parameters, the results were obtained. We are able to detect the specified skin disease from the given classes with an overall accuracy of 90% and the loss of 0.3 which shows the effectiveness of the model.