A Novel Fusion Technique for Early Detection of Alopecia Areata Using ResNet-50 and CRSHOG

The study introduces AlopeciaDet, a novel feature fusion technique for early detection of Alopecia Areata using camera images, which combines CRSHOG features with those from the ResNet-50 model. This approach leverages the geometric properties of rhombus shapes and HOG to enhance pattern recognition in images, specifically targeting the round or oval-shaped patches characteristic of Alopecia Areata. The technique achieved a high accuracy of 99.45% with an error rate of 0.55% using an Artificial Neural Network, outperforming existing methods in the field.