# Glaucoma Detection From Raw Circumapillary OCT Images Using Fully Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2006.00027 · HTML: https://mlsanity.com/paper/2006.00027/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2020.

## Built on by (most cited first, 4 of 4)

- [Dataset and Evaluation algorithm design for GOALS Challenge](https://mlsanity.com/paper/2207.14447/) (2022)
- [Circumpapillary OCT-Focused Hybrid Learning for Glaucoma Grading Using Tailored Prototypical Neural Networks](https://mlsanity.com/paper/2106.13551/) (2021)
- [A self-training framework for glaucoma grading in OCT B-scans](https://mlsanity.com/paper/2111.11771/) (2021)
- [Spatial-aware Transformer-GRU Framework for Enhanced Glaucoma Diagnosis from 3D OCT Imaging](https://mlsanity.com/paper/2403.05702/) (2024)

## Abstract (arXiv)

Nowadays, glaucoma is the leading cause of blindness worldwide. We propose in this paper two different deep-learning-based approaches to address glaucoma detection just from raw circumpapillary OCT images. The first one is based on the development of convolutional neural networks (CNNs) trained from scratch. The second one lies in fine-tuning some of the most common state-of-the-art CNNs architectures. The experiments were performed on a private database composed of 93 glaucomatous and 156 normal B-scans around the optic nerve head of the retina, which were diagnosed by expert ophthalmologists. The validation results evidence that fine-tuned CNNs outperform the networks trained from scratch when small databases are addressed. Additionally, the VGG family of networks reports the most promising results, with an area under the ROC curve of 0.96 and an accuracy of 0.92, during the prediction of the independent test set.