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

Krishnamoorthy V; Sivanantham S; Akshaya V; Nivedha S; Sivakumar Depuru; Manikandan M

doi:10.35882/jeeemi.v7i2.706

Krishnamoorthy V Department of Computer Science & Engineering, Sri Venkateswara College of Engineering, Chennai, India https://orcid.org/0009-0007-4840-5671
Sivanantham S Department of Computer Science & Engineering, Saveetha School of Engineering, SIMATS, Chennai, India https://orcid.org/0000-0001-5215-8211
Akshaya V Department of Artificial Intelligence & Machine Learning, Rajalakshmi Engineering College, Chennai, India https://orcid.org/0009-0003-9822-0035
Nivedha S Department of Information Technology, Bannari Amman Institute of Technology, Erode, India https://orcid.org/0009-0004-6158-4684
Sivakumar Depuru Department of Computer Science and Engineering, Mohan Babu University, Tirupati, India https://orcid.org/0000-0001-5635-6039
Manikandan M Department of Computer Science & Engineering, Saveetha School of Engineering, SIMATS, Chennai, India https://orcid.org/0000-0003-1081-0958

DOI: https://doi.org/10.35882/jeeemi.v7i2.706

Keywords: Attention-gated U-Net, Deep learning, Glaucoma detection, Vision Transformer, Wavelet scattering

Abstract

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.

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