Glaucoma Detection Dataset with OCT and Fundus Images

4.1 GB of retinal images organized per-patient for glaucoma-related study. The dataset includes fundus images and Optical Coherence Tomography scans of left and right eyes, with a folder of processed labels and original annotated data. Personal identifiers have been removed, and the dataset is intended for academic use.

Use Cases

• Train image classification models for glaucoma detection based on fundus images.
• Develop multimodal diagnostic algorithms based on OCT scans.
• Study real-world variability in medical imaging data based on incomplete patient records.
• Benchmark machine learning models using the provided processed labels.

Strengths

• Dataset size is 4.1 GB, indicating a substantial collection of image files.
• Includes two complementary imaging modalities: fundus images and OCT scans.
• Contains structured label files (labels.csv and original_data.xlsx) corresponding to the images.
• Personal identifiers have been removed, enhancing privacy compliance.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Not all patients have all image types, which may introduce data gaps for some analyses.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

figshare

Freshness

Last updated 2026-04-20 08:50:04; freshness should be verified.