Attention Balanced Multi-Dimension Multi-Task Deep Learning for Alopecia Recognition

Abstract

Objective: To increase the accuracy of Alopecia Areata (AA) classification by learning local and global features across AA images and scalp hair images. Methods: An Attention-based Balanced Multi-Task Deep (AB-MTDeep) learning system is proposed. In this system, the MTDeep model incorporates both Multi-Task Learning (MTL) and Cross-Residual Learning (CRL) to simultaneously train hair and scalp images for recognizing AA conditions. In MTL, a new shared encoder is added to the MTDeep model, whereas in CRL, cross-residual layers are added to improve the model's efficiency. According to this learning, both local and global features are learned at multiple scales, as well as, concatenated to get the cross-feature representation. Such features are then classified by the softmax classifier to recognize AA conditions. Findings: Finally, the test outcomes demonstrate that the AB-MTDeep system on hair and scalp image databases realizes an accuracy of 95.11% compared to all other classical systems. Novelty: This model has considerably increased the accuracy of classifying AA conditions. Thus, it represents a promising classifier for AA classification.