---
license: cc-by-4.0
task_categories:
- image-classification
tags:
- medical
- stroke
- ct-scan
- data-augmentation
language:
- en
pretty_name: Stroke Classification Dataset (CT Scans)
---

# Stroke Classification: Pre-processed Brain CT Dataset

### Related resources

- [Live demo](https://huggingface.co/spaces/melisklc0/stroke-classification)
- [Training code](https://github.com/melisklc0/Stroke-Classification)
- [Distilled model](https://huggingface.co/melisklc0/efficientnet-b0-stroke-distilled)

## Summary

- **Task:** Binary CT image classification (`No-Stroke` = 0, `Stroke` = 1)
- **Dataset Size:** ~16.5k images per fold, ~49.5k files across folds on disk. ~3 GB total 
- **Structure:** 3-fold cross-validation (`Fold1`, `Fold2`, `Fold3`), each containing `train/` and `test/` splits
- **Labeling Strategy:** Original classes (`No Stroke`, `Bleeding`, `Ischemia`) were converted into a binary setup by merging `Bleeding` and `Ischemia` into a single `Stroke` class for emergency triage

## Preprocessing Pipeline

1. Merged `Bleeding` and `Ischemia` into a unified `Stroke` class.
2. Applied stratified 3-fold cross-validation with separate `train/` and `test/` sets.
3. Balanced test sets to ~750 images per class per fold.
4. Applied data augmentation on training sets:
   - Rotation (±10°)
   - Zooming
   - Translation
   - Horizontal flipping
   resulting in ~7,500 images per class per fold.
  
## Repository Structure

The dataset is organized to support cross-validation training workflows and external generalization testing seamlessly:

* **`dataset/`**: Contains all pre-processed, augmented, and validation CT scan images.
  * `External_Dataset/`: An independent dataset (sourced from Kaggle) used purely for external validation and testing model generalization across different scanner calibrations.
  * `Fold1/`: Training and validation split 1.
  * `Fold2/`: Training and validation split 2.
  * `Fold3/`: Training and validation split 3.

*Inside each fold and the external dataset, the images are categorized into two sub-folders: `Stroke` and `No-Stroke`.*   
*Train on `FoldN/train`, validate on `FoldN/test`. Rotate folds for 3-fold cross-validation.*

## Source Data & Attribution

The data utilized to build this augmented dataset originates from two primary sources. In compliance with open data policies, please find the attributions below:

1. **Primary Dataset (Turkish Ministry of Health):** The raw CT scans utilized to build the core augmented training folds were obtained from the Open Data Portal of the Republic of Turkey Ministry of Health. 
   * *Original Source:* [Open Data Portal of the Turkish Ministry of Health](https://acikveri.saglik.gov.tr/Home/DataSetDetail/1)
2. **External Validation Dataset (Kaggle):** An external dataset was used purely to test the robust generalization of the trained models across different scanner calibrations.
   * *Original Source:* [Head CT Hemorrhage Dataset by Felipe Kitamura](https://www.kaggle.com/datasets/felipekitamura/head-ct-hemorrhage)

## Licensing & Usage

This processed dataset is distributed under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license. 

You are free to share and adapt this material for any purpose, even commercially, under the following terms:
* **Attribution:** You must give appropriate credit to the original data sources (mentioned above) and indicate that this is an augmented, pre-processed version of the original files.

*Disclaimer: This dataset is provided for research and educational purposes. It is not intended to replace professional medical diagnosis or serve as a standalone clinical tool.*

## Authors
* **Melis Kılıç**
* **Esra Koç**

**Advisor:** Assoc. Prof. Dr. Kali Gürkahran