| --- |
| 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 |
