FJA-Net: A Fuzzy Joint Attention Guided Network for Classification of Glaucoma Stages

Glaucoma is a progressive eye disorder that can lead to permanent vision loss if not identified and treated promptly. Thus, timely glaucoma detection is paramount to developing a more efficient treatment plan and saving vision loss. Despite the promising performance achieved by deep learning methods in specific convolutional neural networks (CNNs) for glaucoma screening using fundus images, they are confined to binary classification tasks (i.e., healthy versus glaucoma) and cannot detect glaucoma stages. However, it is challenging to diagnose the glaucoma stages accurately due to considerable interstage similarities, the subtle changes in the size of lesions, and the presence of irrelevant features. Moreover, fundus images encompass significant uncertain information, which cannot be effectively captured through conventional CNNs. To solve these problems, we present a novel fuzzy joint attention-guided network called FJA-Net for the screening of glaucoma stages. Specifically, we introduce a fuzzy joint attention module (FJAM) on top of a backbone, composed of a local–global channel and spatial attention block, to learn comprehensive feature correlations along the relevant channels and spatial positions, each followed by a fuzzy layer to reduce the uncertainty in the feature representations. The FJAM aids in learning stage-specific and fine-grained features from critical regions of the fundus images. In addition, we propose a combined loss function to train the parameters of our FJA-Net to ensure better generalization and robustness. We evaluate the proposed model on two datasets, and the results of the comparative analysis demonstrate that our FJA-Net outperforms state-of-the-art CNN-based glaucoma classification approaches.