README.md

---
task_categories:
- image-classification
tags:
- medical
pretty_name: Standardized Multi-Channel Dataset for Glaucoma
size_categories:
- 10K<n<100K
license: mit
---

# Dataset Card for Dataset Name

<!-- Provide a quick summary of the dataset. -->

All the images of the dataset come from [this kaggle dataset](https://www.kaggle.com/datasets/deathtrooper/multichannel-glaucoma-benchmark-dataset). 
Only fundus images have been collected and some minor modifications have been made to the metadata.
All credit goes to the original authors and the contributor on Kaggle.

## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->

Standardized Multi-Channel Dataset for Glaucoma (SMDG-19) is a collection and standardization of 19 public datasets, comprised of full-fundus glaucoma images and associated basic image metadata. This dataset is designed to be exploratory and open-ended with multiple use cases. This dataset is the largest public repository of fundus images with glaucoma.

The objective of this dataset is a machine learning-ready dataset for glaucoma-related applications. Using the help of the community, new open-source glaucoma datasets will be reviewed for standardization and inclusion in this dataset.

- **Curated by:** [Riley Kiefer](https://www.kaggle.com/deathtrooper)
- **Shared by:** [Riley Kiefer](https://www.kaggle.com/deathtrooper)
- **License:** MIT

### Dataset Sources

<!-- Provide the basic links for the dataset. -->

- **Repository:**
  - [kaggle repo](https://www.kaggle.com/datasets/deathtrooper/multichannel-glaucoma-benchmark-dataset)
  - [Github repo](https://github.com/TheBeastCoding/standardized-multichannel-dataset-glaucoma)
- **Paper:** There is no specific paper associated with the dataset, but the author has contributed to closely related papers (see citation).

## Uses

<!-- Address questions around how the dataset is intended to be used. -->


### Direct Use

<!-- This section describes suitable use cases for the dataset. -->

Glaucoma classification.

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->

[More Information Needed]

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

| Dataset                                                               | 0 (Non-Glaucoma) | 1 (Glaucoma) | -1 (Glaucoma Suspect) |
|-----------------------------------------------------------------------|------------------|--------------|-----------------------|
| BEH (Bangladesh Eye Hospital)                                          | 463              | 171          | 0                     |
| CRFO-v4                                                               | 31               | 48           | 0                     |
| DR-HAGIS (Diabetic Retinopathy, Hypertension, Age-related macular degeneration and Glacuoma ImageS) | 0                | 10           | 0                     |
| DRISHTI-GS1-TRAIN                                                     | 18               | 32           | 0                     |
| DRISHTI-GS1-TEST                                                      | 13               | 38           | 0                     |
| EyePACS-AIROGS                                                         | 0                | 3269         | 0                     |
| FIVES                                                                 | 200              | 200          | 0                     |
| G1020                                                                  | 724              | 296          | 0                     |
| HRF (High Resolution Fundus)                                           | 15               | 15           | 0                     |
| JSIEC-1000 (Joint Shantou International Eye Center)                   | 38               | 0            | 13                    |
| LES-AV                                                                 | 11               | 11           | 0                     |
| OIA-ODIR-TRAIN                                                         | 2932             | 197          | 18                    |
| OIA-ODIR-TEST-ONLINE                                                   | 802              | 58           | 25                    |
| OIA-ODIR-TEST-OFFLINE                                                  | 417              | 36           | 9                     |
| ORIGA-light                                                            | 482              | 168          | 0                     |
| PAPILA                                                                 | 333              | 87           | 68                    |
| REFUGE1-TRAIN (Retinal Fundus Glaucoma Challenge 1 Train)              | 360              | 40           | 0                     |
| REFUGE1-VALIDATION (Retinal Fundus Glaucoma Challenge 1 Validation)    | 360              | 40           | 0                     |
| sjchoi86-HRF                                                           | 300              | 101          | 0                     |
| **Total**                                                              | **7499**         | **4817**     | **133**               |

The original dataset is not splitted. Training, validation and test partitions were created randomly with proportions 70:15:15.

## Dataset Creation

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->

- Full fundus images (and corresponding segmentation maps) are standardized using a novel algorithm (Citation 1) by cropping the background, centering the fundus image, padding missing information, and resizing to 512x512 pixels. This standardization ensures that the most amount of foreground information is prevalent during the resizing process for machine-learning-ready image processing.
- Each available metadata text is standardized by provided each fundus image as a row and each fundus attribute as a column in a CSV file

### Source Data

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

[More Information Needed]

#### Who are the source data producers?

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->

Researchers and creators of the 19 datasets contained in SMDG-19.

#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

The original dataset includes optic disc segmentation, optic cup segmentation, blood vessel segmentation, and any more per-instance text metadata. 
The version hosted here **only contains fundus images**.

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

## Citation 

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

SMDG; `@dataset{smdg,
  title={SMDG, A Standardized Fundus Glaucoma Dataset},
  url={https://www.kaggle.com/ds/2329670},
  DOI={10.34740/KAGGLE/DS/2329670},
  publisher={Kaggle},
  author={Riley Kiefer},
  year={2023}
}`


Related papers

  1. Kiefer, Riley, et al. "A Catalog of Public Glaucoma Datasets for Machine Learning Applications: A detailed description and analysis of public glaucoma datasets available to machine learning engineers tackling glaucoma-related problems using retinal fundus images and OCT images." Proceedings of the 2023 7th International Conference on Information System and Data Mining. 2023.

  2. R. Kiefer, M. Abid, M. R. Ardali, J. Steen and E. Amjadian, "Automated Fundus Image Standardization Using a Dynamic Global Foreground Threshold Algorithm," 2023 8th International Conference on Image, Vision and Computing (ICIVC), Dalian, China, 2023, pp. 460-465, doi: 10.1109/ICIVC58118.2023.10270429.

  3. Kiefer, Riley, et al. "A Catalog of Public Glaucoma Datasets for Machine Learning Applications: A detailed description and analysis of public glaucoma datasets available to machine learning engineers tackling glaucoma-related problems using retinal fundus images and OCT images." Proceedings of the 2023 7th International Conference on Information System and Data Mining. 2023.

  4. R. Kiefer, J. Steen, M. Abid, M. R. Ardali and E. Amjadian, "A Survey of Glaucoma Detection Algorithms using Fundus and OCT Images," 2022 IEEE 13th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada, 2022, pp. 0191-0196, doi: 10.1109/IEMCON56893.2022.9946629.

## Glossary

<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->

[More Information Needed]

## More Information 

[More Information Needed]

## Dataset Card Authors 

bumbledeep