EDID / README.md
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metadata
license: cc-by-4.0
task_categories:
  - image-classification
tags:
  - medical
  - ophthalmology
  - retina
  - fundus
  - eye-disease
splits:
  - train
  - validation
  - test
dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': CSCR
            '1': DiabeticRetinopathy
            '2': DiscEdema
            '3': Glaucoma
            '4': Healthy
            '5': MacularScar
            '6': Myopia
            '7': Pterygium
            '8': RetinalDetachment
            '9': RetinitisPigmentosa
  splits:
    - name: train
      num_bytes: 637783802
      num_examples: 3734
    - name: validation
      num_bytes: 136994645
      num_examples: 800
    - name: test
      num_bytes: 136336895
      num_examples: 801
  download_size: 910198022
  dataset_size: 911115342
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*

EDID — Eye Disease Fundus Image Dataset

A curated dataset of retinal fundus images for automated eye disease classification.
The original Dataset shared in paper "A dataset of color fundus images for the detection and classification of eye diseases", doi: https://doi.org/10.1016/j.dib.2024.110979.
Link to original dataset: https://data.mendeley.com/datasets/s9bfhswzjb/1
All credits goes to authors of mentioned paper.

Dataset Details

Please visit links above for more information about data collection, source location and limitations.


No augmentation is applied to dataset.
Original image size: 2004 × 1690 px
Rezised to: 1024 × 1024 px
Total images: 5335
Split: 70% train, 15% val, 15% test
Number of classes: 10

Classes

  • CSCR (Central Serous Chorioretinopathy)
  • DiabeticRetinopathy
  • DiscEdema
  • Glaucoma
  • Healthy
  • MacularScar
  • Myopia
  • Pterygium
  • RetinalDetachment
  • RetinitisPigmentosa

Citation

If you are using this dataset, please consider citing original paper (https://doi.org/10.1016/j.dib.2024.110979) and mention bekzatn/EDID.