# Exploring Deep Learning Techniques for Glaucoma Detection: A Comprehensive Review

arXiv: https://arxiv.org/abs/2311.01425 · HTML: https://mlsanity.com/paper/2311.01425/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 58 references that are in the map.
- First posted on arXiv 2 Nov 2023.

## Builds on (most cited first, 5 of 5)

- [Disc-aware Ensemble Network for Glaucoma Screening from Fundus Image](https://mlsanity.com/paper/1805.07549/) (2018)
- [IC-U-Net: A U-Net-based Denoising Autoencoder Using Mixtures of Independent Components for Automatic EEG Artifact Removal](https://mlsanity.com/paper/2111.10026/) (2021)
- [Deep Learning and Computer Vision for Glaucoma Detection: A Review](https://mlsanity.com/paper/2307.16528/) (2023)
- [A CNN-LSTM Combination Network for Cataract Detection using Eye Fundus Images](https://mlsanity.com/paper/2210.16093/) (2022)
- [M-Net with Bidirectional ConvLSTM for Cup and Disc Segmentation in Fundus Images](https://mlsanity.com/paper/2104.03549/) (2021)

## Abstract (arXiv)

Glaucoma is one of the primary causes of vision loss around the world, necessitating accurate and efficient detection methods. Traditional manual detection approaches have limitations in terms of cost, time, and subjectivity. Recent developments in deep learning approaches demonstrate potential in automating glaucoma detection by detecting relevant features from retinal fundus images. This article provides a comprehensive overview of cutting-edge deep learning methods used for the segmentation, classification, and detection of glaucoma. By analyzing recent studies, the effectiveness and limitations of these techniques are evaluated, key findings are highlighted, and potential areas for further research are identified. The use of deep learning algorithms may significantly improve the efficacy, usefulness, and accuracy of glaucoma detection. The findings from this research contribute to the ongoing advancements in automated glaucoma detection and have implications for improving patient outcomes and reducing the global burden of glaucoma.