FRCNN based Deep Learning for Identification and Classification of Alopecia Areata

The study proposed a Faster Residual Convolutional Neural Network (FRCNN) model to improve the detection and diagnosis of alopecia areata (AA) and other scalp conditions using deep learning techniques. The FRCNN model utilized a Region-Of-Interest (ROI) projection layer to enhance recognition accuracy. Scalp and AA images were processed through deep convolutional layers and ROI pooling to extract feature maps, which were then classified using a softmax classifier. The model achieved an accuracy of 84.3% on hair and scalp image databases, outperforming other models in recognizing various conditions of AA.