Glaucoma is a leading cause of irreversible blindness, often diagnosed
too late due to subtle symptoms and the reliance on manual evaluation
of retinal images. Early and accurate detection is essential for
preventing vision loss, yet conventional deep learning methods face
challenges in feature generalization and spatial attention. Existing
convolutional neural network (CNN)-based and standard GAN
approaches often underperform in preserving subtle pathological
features and attention mechanisms required for robust glaucoma
detection. Moreover, the lack of residual attention integration in
multihead architectures limits diagnostic precision. This study
proposes a novel deep learning model termed Residual Multihead
Multilayer Attention GANs (RMMLA-GANs) that combines the
strengths of Generative Adversarial Networks (GANs), residual
learning, and multihead attention mechanisms. The generator
incorporates multi-layer residual attention blocks and self-attention
heads to enhance critical feature localization. A contrastive
discriminator improves inter-class feature separability. The model was
trained and validated using the RIM-ONE and DRISHTI-GS1 datasets.
Our RMMLA-GANs model achieved superior performance over four
existing hybrid approaches: Attention U-Net, Dense-GAN, ResNet-
GAN, and VGG-GAN. It achieved an accuracy of 96.7%, sensitivity of
97.1%, specificity of 95.4%, AUC of 0.982, and F1-score of 96.3%,
outperforming the best existing method by 3.2% in AUC and 2.8% in
sensitivity.
P. Neethu Prabhakaran1, Dini Davis2, P.P. Priya3, Mini Mohan4, P.A. Shemitha5
IES College of Engineering, India1,2,3,4, Zayed University, Dubai Campus, United Arab Emirates5
Glaucoma Diagnosis, Deep Learning, Attention GANs, Residual Learning, Retinal Imaging
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 15 , Issue: 4 , Pages: 3606 - 3612 )
Date of Publication :
May 2025
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70
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