--- language: - en license: other # Usage restricted under Kaggle & EyePACS terms, not standard open license tags: - medical - ophthalmology - fundus-image - image-classification - diabetic-retinopathy - dr-grading - large-scale-dataset task_categories: - image-classification task_ids: - multi-class-image-classification # 5 severity grades (0-4) pretty_name: EyePACS (Diabetic Retinopathy Fundus Image Dataset) size_categories: - 10K Merged Dataset Samples

Image: EYEPACS Preprocess Samples.

--- ## πŸ“˜ 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. --- ## πŸ“Š Dataset Summary | Field | Details | |--------|----------| | **Task** | Diabetic retinopathy classification (5 severity grades) | | **Description** | High-resolution color fundus photographs from EyePACS DR screening program. Each image labeled by ophthalmologists using the ICDR scale. | | **Size** | ~88,702 images total (β‰ˆ35k labeled for training, β‰ˆ53k unlabeled for testing) | | **Classes** | 0 = No DR, 1 = Mild, 2 = Moderate, 3 = Severe, 4 = Proliferative DR | | **Image Type** | Macula-centered color fundus photos (640Γ—480 to 5184Γ—3456 px) | | **Source** | EyePACS (USA) via Kaggle Diabetic Retinopathy Detection Challenge (2015) | | **Access** | [Kaggle Dataset](https://www.kaggle.com/c/diabetic-retinopathy-detection/data) | | **License** | Usage restricted under Kaggle & EyePACS terms | --- ## 🧱 Dataset Structure ```test eyepacs/ β”œβ”€β”€ images/ β”‚ β”œβ”€β”€ train/ β”‚ β”‚ β”œβ”€β”€ 00001_left.jpg β”‚ β”‚ β”œβ”€β”€ 00001_right.jpg β”‚ └── test/ β”œβ”€β”€ labels.csv # image_id, eye(L/R), dr_grade (0–4) β”œβ”€β”€ README.md └── LICENSE.txt ``` --- ## 🧩 Label Details - Labels follow the **International Clinical Diabetic Retinopathy (ICDR)** grading system. - **Class imbalance** is significant β€” most images show no DR. - Labels were assigned by certified ophthalmologists; minor label noise may exist. --- ## βš™οΈ Preprocessing Recommendations - Crop to the circular fundus region and remove borders - Resize to consistent resolution (e.g. 1024Γ—1024) - Normalize illumination and contrast - Exclude blurred or ungradable images --- ## πŸ’‘ 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** β€” verify Kaggle/EyePACS terms before publishing images --- ## πŸ“„ Citation If you use the dataset, cite: > Kaggle and EyePACS. *β€œDiabetic Retinopathy Detection.”* Kaggle Competition, 2015. > [https://www.kaggle.com/c/diabetic-retinopathy-detection](https://www.kaggle.com/c/diabetic-retinopathy-detection) --- ## πŸ”— References - EyePACS Official Data Page β€” [https://www.eyepacs.com/data-analysis](https://www.eyepacs.com/data-analysis) - Kaggle: *Diabetic Retinopathy Detection* β€” [https://www.kaggle.com/c/diabetic-retinopathy-detection](https://www.kaggle.com/c/diabetic-retinopathy-detection) - Research overview: *Transfer Learning Based Classification of Diabetic Retinopathy on the Kaggle EyePACS Dataset*, ResearchGate, 2021. ---