A Novel Deep Learning Framework for Enhanced Glaucoma Detection Using Attention-Gated U-Net, Deep Wavelet Scattering, and Vision Transformers

Globally, Glaucoma is a major cause of permanent blindness, and maintaining eyesight depends on early detection. Here, a brand-new deep-learning system for glaucoma prediction. In this work, we offer a novel deep-learning approach for enhanced glaucoma prediction that uses a denoising generative adversarial network for preprocessing the input image is provided, later the segmentation is carried out by Attention-Gated U-Net with Dilated Convolutions to segment the optic cup and optic disc. Feature Extraction Using a Deep Wavelet Scattering Network and finally the glaucoma classification is carried out by the Vision Transformers. An attention-gated U-Net with dilated convolutions for segmentation, which improves the accuracy of optic disc and cup boundaries by 7% compared to conventional U-Net methods is introduced. A Deep Wavelet Scattering Network (DWSN) for feature extraction that achieves a 5% improvement in feature discrimination over conventional CNNs by capturing multiscale texture and structural information is suggested. Lastly, ViT, which is based on transfer learning, is used for classification; it has a 94.6% accuracy rate, a 93.8% sensitivity rate, and a 95.2% specificity rate. The suggested approach outperformed CNN-based models by improving by about 4% on all criteria. The system achieved an F1 score of 0.95 and an AUC (Area Under Curve) of 0.96 when tested on publicly accessible glaucoma datasets. Multi-stage deep-learning processing for glaucoma prediction by integrating a denoising generative adversarial network for image preprocessing, Attention-Gated U-Net with Dilated Convolutions for exact optic cup and disc segmentation, deep wavelet scattering for feature extraction, and Vision Transformers for glaucoma classification.