AB-MTEDeep Classifier Trained with AAGAN for the Identification and Classification of Alopecia Areata

Abstract

Artificial Intelligence (AI) is widely used in dermatology to analyze trichoscopy imaging and assess Alopecia Areata (AA) and scalp hair problems. From this viewpoint, the Attention-based Balanced Multi-Tasking Ensembling Deep (AB-MTEDeep) network was developed, which combined the Faster Residual Convolutional Neural Network (FRCNN) and Long Short-Term Memory (LSTM) network with cross residual learning to classify scalp images into different AA classes. This article presents a new data augmentation model called AA-Generative Adversarial Network (AA-GAN) to produce a huge number of images from a set of input images. The structure of AA-GAN and its loss functions are comparable to those of standard GAN, which encompasses a generator and a discriminator network. To generate high-quality AA structure-based images, the generator was trained to extract the 2D orientation and confidence maps along with the bust depth map from real hair and scalp images. The discriminator was also used to separate real from generated images, which were provided as feedback to the generator to create synthetic images that are extremely close to the real input images. The created images were used to train the AB-MTEDeep model for AA classification. Finally, the experimental results exhibited that the AA-GAN-AB-MTEDeep achieved 96.94% accuracy.