Early Glaucoma Detection using Deep Learning with Multiple Datasets of Fundus Images
This work addresses early detection of glaucoma, a leading cause of blindness, by providing a reproducible and scalable method, though it appears incremental as it builds on existing architectures and datasets.
The paper tackled early glaucoma detection by developing a deep learning pipeline using EfficientNet-B0, trained across multiple fundus image datasets, and achieved strong discriminative performance with minimal preprocessing.
Glaucoma is a leading cause of irreversible blindness, but early detection can significantly improve treatment outcomes. Traditional diagnostic methods are often invasive and require specialized equipment. In this work, we present a deep learning pipeline using the EfficientNet-B0 architecture for glaucoma detection from retinal fundus images. Unlike prior studies that rely on single datasets, we sequentially train and fine-tune our model across ACRIMA, ORIGA, and RIM-ONE datasets to enhance generalization. Our experiments show that minimal preprocessing yields higher AUC-ROC compared to more complex enhancements, and our model demonstrates strong discriminative performance on unseen datasets. The proposed pipeline offers a reproducible and scalable approach to early glaucoma detection, supporting its potential clinical utility.