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

Alopecia Areata is an autoimmune disorder where the body's immune system attacks normal cells instead of intruders, leading to hair loss. If not detected early, it can progress to complete scalp baldness (Alopecia Totalis) or total body hair loss (Alopecia Universalis). Therefore, early detection of Alopecia Areata is crucial. Computer vision and deep learning techniques have been used for the last few years in the field of dermatology to detect different relevant diseases. We proposed a robust feature fusion technique, named AlopeciaDet for the timely detection of Alopecia Areata using camera images instead of dermoscopic images that require specialized equipment. AlopeciaDet combines Corner Rhombus Shape HOG (CRSHOG) features with those extracted from the ResNet-50 pre-trained model to detect Alopecia Areata with high accuracy by using the Dermenet dataset. The geometric properties of rhombus shapes make them useful for recognizing patterns in an image. HOG captures local object appearance and shape by computing the distribution of intensity gradients in localized portions of the image. We combined these characteristics into CRSHOG. Alopecia Areata, on the other hand, is characterized by distinctive patterns and shapes of hair loss, with the most common feature being round or oval-shaped patches. These patches can vary in size and usually have well-defined, sharp edges. Consequently, our proposed CRSHOG significantly improves the extraction of local information from images of affected areas. It achieves this by integrating sign and magnitude data, thereby enhancing discrimination capabilities for texture classification tasks. Finally, the magnitudes and directions of these pixel values are calculated. We achieved an accuracy of 99.45% with an error rate of 0.55% using Artificial Neural Network. These results surpass the accuracy of current state-of-the-art techniques in this field.