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ctmedtech/EYEPACS

EyePACS — Diabetic Retinopathy Fundus Image Dataset Image: EYEPACS Preprocess Samples. 📘 Overview EyePACS (Eye Picture Archive Communication System) is a large-scale collection of retinal fundus images used for automated diabetic retinopathy (DR) detection.It formed the basis of the Kaggle Diabetic Retinopathy Detection challenge, enabling research into DR classification and screening. The… See the full description on the dataset page: https://huggingface.co/datasets/ctmedtech/EYEPACS.

sourceHugging Faceotherupdated 11mo agoView on Hugging Face
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Dataset Card

EyePACS — Diabetic Retinopathy Fundus Image Dataset

<table align="center"> <tr> <td width="100%" align="center"> <img src="rmimages/MergedFundusImageswith_Caption.jpg" alt="Merged Dataset Samples" style="max-width: 100%; height: auto;"> <br> <p><strong>Image:</strong> EYEPACS Preprocess Samples.</p> </td> </tr> </table>

📘 Overview

EyePACS (Eye Picture Archive Communication System) is a large-scale collection of retinal fundus images used for automated diabetic retinopathy (DR) detection. It formed the basis of the Kaggle Diabetic Retinopathy Detection challenge, enabling research into DR classification and screening.

The dataset includes macula-centered color fundus images from real-world clinical screenings under varied imaging conditions.

📊 Dataset Summary

FieldDetails
TaskDiabetic retinopathy classification (5 severity grades)
DescriptionHigh-resolution color fundus photographs from EyePACS DR screening program. Each image labeled by ophthalmologists using the ICDR scale.
Size~88,702 images total (≈35k labeled for training, ≈53k unlabeled for testing)
Classes0 = No DR, 1 = Mild, 2 = Moderate, 3 = Severe, 4 = Proliferative DR
Image TypeMacula-centered color fundus photos (640×480 to 5184×3456 px)
SourceEyePACS (USA) via Kaggle Diabetic Retinopathy Detection Challenge (2015)
AccessKaggle Dataset
LicenseUsage restricted under Kaggle & EyePACS terms

🧱 Dataset Structure

test
eyepacs/
├── images/
│ ├── train/
│ │ ├── 00001_left.jpg
│ │ ├── 00001_right.jpg
│ └── test/
├── labels.csv # image_id, eye(L/R), dr_grade (0–4)
├── README.md
└── LICENSE.txt

🧩 Label Details

  • —Labels follow the International Clinical Diabetic Retinopathy (ICDR) grading system.
  • —Class imbalance is significant — most images show no DR.
  • —Labels were assigned by certified ophthalmologists; minor label noise may exist.

⚙️ Preprocessing Recommendations

  • —Crop to the circular fundus region and remove borders
  • —Resize to consistent resolution (e.g. 1024×1024)
  • —Normalize illumination and contrast
  • —Exclude blurred or ungradable images

💡 Research Applications

  • —DR detection and severity classification
  • —Automated retinal screening systems
  • —Transfer learning and robustness testing across imaging conditions
  • —Comparative studies with datasets like MESSIDOR, DDR, and APTOS

⚠️ Notes & Limitations

  • —Significant class imbalance (majority = No DR)
  • —Variations in camera type, exposure, and focus
  • —Some label noise and ungradable images present
  • —Redistribution may be restricted — verify Kaggle/EyePACS terms before publishing images

📄 Citation

If you use the dataset, cite:

Kaggle and EyePACS. “Diabetic Retinopathy Detection.” Kaggle Competition, 2015. https://www.kaggle.com/c/diabetic-retinopathy-detection

🔗 References