GARDNet: Robust Multi-View Network for Glaucoma Classification in Color Fundus Images

TL;DR

GARDNet is a multi-view deep learning network that improves glaucoma detection accuracy from fundus images, achieving state-of-the-art results on multiple datasets to aid early diagnosis and screening.

Contribution

The paper introduces GARDNet, a novel multi-view deep learning model with advanced preprocessing for glaucoma classification, outperforming existing methods.

Findings

Achieved AUC of 0.92 on Rotterdam EyePACS dataset.

Fine-tuned model reached AUC of 0.9308 on RIM-ONE DL dataset.

Outperformed the previous state-of-the-art AUC of 0.9272.

Abstract

Glaucoma is one of the most severe eye diseases, characterized by rapid progression and leading to irreversible blindness. It is often the case that diagnostics is carried out when one's sight has already significantly degraded due to the lack of noticeable symptoms at early stage of the disease. Regular glaucoma screenings of the population shall improve early-stage detection, however the desirable frequency of etymological checkups is often not feasible due to the excessive load imposed by manual diagnostics on limited number of specialists. Considering the basic methodology to detect glaucoma is to analyze fundus images for the optic-disc-to-optic-cup ratio, Machine Learning algorithms can offer sophisticated methods for image processing and classification. In our work, we propose an advanced image pre-processing technique combined with a multi-view network of deep classification models to categorize glaucoma. Our Glaucoma Automated Retinal Detection Network (GARDNet) has been successfully tested on Rotterdam EyePACS AIROGS dataset with an AUC of 0.92, and then additionally fine-tuned and tested on RIM-ONE DL dataset with an AUC of 0.9308 outperforming the state-of-the-art of 0.9272. Our code is available on https://github.com/ahmed1996said/gardnet