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README.md

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+# EyePACS — Diabetic Retinopathy Fundus Image Dataset
+
+<table align="center">
+    <tr>
+        <td width="100%" align="center">
+            <img src="rm_images/Merged_Fundus_Images_with_Caption.jpg" alt="Merged Dataset Samples" style="max-width: 100%; height: auto;">
+            <br>
+            <p><strong>Image:</strong> EYEPACS Preprocess Samples.</p>
+        </td>
+    </tr>
+</table>
+
+> *Illustrative fundus photographs similar to those in EyePACS. These are not the actual EyePACS competition images.*
+
+---
+
+## 📘 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
+
+| 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 (≈35k labeled for training, ≈53k unlabeled for testing) |
+| **Classes** | 0 = No DR, 1 = Mild, 2 = Moderate, 3 = Severe, 4 = Proliferative DR |
+| **Image Type** | Macula-centered color fundus photos (640×480 to 5184×3456 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/
+├── 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](https://www.kaggle.com/c/diabetic-retinopathy-detection)
+
+---
+
+## 🔗 References
+- EyePACS Official Data Page — [https://www.eyepacs.com/data-analysis](https://www.eyepacs.com/data-analysis)  
+- Kaggle: *Diabetic Retinopathy Detection* — [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.  
+
+---