Real-Time Glaucoma Detection from Digital Fundus Images using Self-ONNs

TL;DR

This paper introduces Self-ONNs for early glaucoma detection from fundus images, demonstrating superior accuracy and reduced computational complexity compared to CNNs, especially effective with limited labeled data.

Contribution

The study proposes Self-ONNs as a novel, efficient alternative to CNNs for glaucoma detection, showing improved performance on benchmark datasets.

Findings

Self-ONNs outperform CNNs in detection accuracy.

Self-ONNs require less computational resources.

Effective with scarce labeled data.

Abstract

Glaucoma leads to permanent vision disability by damaging the optical nerve that transmits visual images to the brain. The fact that glaucoma does not show any symptoms as it progresses and cannot be stopped at the later stages, makes it critical to be diagnosed in its early stages. Although various deep learning models have been applied for detecting glaucoma from digital fundus images, due to the scarcity of labeled data, their generalization performance was limited along with high computational complexity and special hardware requirements. In this study, compact Self-Organized Operational Neural Networks (Self- ONNs) are proposed for early detection of glaucoma in fundus images and their performance is compared against the conventional (deep) Convolutional Neural Networks (CNNs) over three benchmark datasets: ACRIMA, RIM-ONE, and ESOGU. The experimental results demonstrate that Self-ONNs not only achieve superior detection performance but can also significantly reduce the computational complexity making it a potentially suitable network model for biomedical datasets especially when the data is scarce.