| --- |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: "modality_dataset.zip" |
| license: cc-by-4.0 |
| task_categories: |
| - image-classification |
| tags: |
| - medical |
| - imaging |
| - ct |
| - mri |
| - xray |
| - ultrasound |
| pretty_name: Generalized Medical Image Modality Dataset |
| size_categories: |
| - 1K<n<10K |
| --- |
| # 🏥 Generalized Medical Image Modality Dataset |
|
|
| A curated, balanced dataset for training **medical imaging modality classifiers**. |
| Contains images from four modalities (CT, MRI, X-Ray, Ultrasound) spanning multiple |
| anatomical regions to ensure robust generalization. |
|
|
| > **Total images:** 9,450 | **Target:** 9,450 |
|
|
| --- |
|
|
| ## 📊 Modality Summary |
|
|
| | Modality | Organ Classes (for Organ Classifier) | Images | Target | % of Total | |
| |----------|--------------------------------------|-------:|-------:|-----------:| |
| | CT | Head, Chest, Abdomen | 2,200 | 2,200 | 23.3% | |
| | MRI | Brain, Spine | 2,000 | 2,000 | 21.2% | |
| | US | Breast, Kidney, Ovary_Pelvis | 2,250 | 2,250 | 23.8% | |
| | XRAY | Chest, Hand, Knee | 3,000 | 3,000 | 31.7% | |
| | **TOTAL** | | **9,450** | **9,450** | **100%** | |
| |
| --- |
| |
| ## 🔬 Per-Organ Breakdown |
| |
| | Modality | Organ | Images | Target | Kaggle Source | Status | |
| |----------|-------|-------:|-------:|---------------|--------| |
| | CT | Abdomen | 1,000 | 1,000 | [nazmul0087/ct-kidney-dataset-normal-cyst-tumor-and-stone](https://www.kaggle.com/datasets/nazmul0087/ct-kidney-dataset-normal-cyst-tumor-and-stone) | ✅ | |
| | CT | Chest | 1,000 | 1,000 | [mohamedhanyyy/chest-ctscan-images](https://www.kaggle.com/datasets/mohamedhanyyy/chest-ctscan-images) | ✅ | |
| | CT | Head | 200 | 200 | [felipekitamura/head-ct-hemorrhage](https://www.kaggle.com/datasets/felipekitamura/head-ct-hemorrhage) | ✅ | |
| | MRI | Brain | 1,000 | 1,000 | [masoudnickparvar/brain-tumor-mri-dataset](https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset) | ✅ | |
| | MRI | Spine | 1,000 | 1,000 | [anoukstein/spider-mri-spine-t2-png](https://www.kaggle.com/datasets/anoukstein/spider-mri-spine-t2-png) | ✅ | |
| | US | Breast | 750 | 750 | [aryashah2k/breast-ultrasound-images-dataset](https://www.kaggle.com/datasets/aryashah2k/breast-ultrasound-images-dataset) | ✅ | |
| | US | Kidney | 750 | 750 | [gurjeetkaurmangat/kidney-ultrasound-images-stone-and-no-stone](https://www.kaggle.com/datasets/gurjeetkaurmangat/kidney-ultrasound-images-stone-and-no-stone) | ✅ | |
| | US | Ovary_Pelvis | 750 | 750 | [orvile/mmotu-ovarian-ultrasound-images-dataset](https://www.kaggle.com/datasets/orvile/mmotu-ovarian-ultrasound-images-dataset) | ✅ | |
| | XRAY | Chest | 1,000 | 1,000 | [paultimothymooney/chest-xray-pneumonia](https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia) | ✅ | |
| | XRAY | Hand | 1,000 | 1,000 | [antonbudnychuk/hand-xray](https://www.kaggle.com/datasets/antonbudnychuk/hand-xray) | ✅ | |
| | XRAY | Knee | 1,000 | 1,000 | [shashwatwork/knee-osteoarthritis-dataset-with-severity](https://www.kaggle.com/datasets/shashwatwork/knee-osteoarthritis-dataset-with-severity) | ✅ | |
|
|
| --- |
|
|
| ## 📁 Folder Structure |
|
|
| ``` |
| modality_dataset/ |
| ├── CT/ |
| │ ├── Chest/ (CT chest scans) |
| │ ├── Head/ (CT head hemorrhage scans) |
| │ └── Abdomen/ (CT kidney / abdominal scans) |
| ├── MRI/ |
| │ ├── Brain/ (Brain tumor MRI) |
| │ └── Spine/ (Spine T2 MRI) |
| ├── XRAY/ |
| │ ├── Chest_Pneumonia/ (Chest X-ray — pneumonia) |
| │ ├── Chest_TB/ (Chest X-ray — tuberculosis) |
| │ └── Hand/ (Hand X-ray) |
| └── US/ |
| ├── Breast/ (Breast ultrasound) |
| ├── Kidney/ (Kidney ultrasound) |
| └── Ovary_Pelvis/ (Ovarian / pelvic ultrasound) |
| ``` |
|
|
| ## 🚀 Usage |
|
|
| Images are packaged in `modality_dataset.zip`. Extract and use directly: |
|
|
| ```python |
| import zipfile |
| with zipfile.ZipFile("modality_dataset.zip", "r") as z: |
| z.extractall("./modality_dataset") |
| ``` |
|
|
| The top-level folder names (`CT`, `MRI`, `XRAY`, `US`) are the **class labels** for |
| the modality classifier. Sub-folders represent the anatomical regions used as data sources. |
|
|
