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PrateekShetty2552/Medical-Modality-Dataset

🏥 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… See the full description on the dataset page: https://huggingface.co/datasets/PrateekShetty2552/Medical-Modality-Dataset.

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Dataset Card

🏥 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	✅
CT	Chest	1,000	1,000	mohamedhanyyy/chest-ctscan-images	✅
CT	Head	200	200	felipekitamura/head-ct-hemorrhage	✅
MRI	Brain	1,000	1,000	masoudnickparvar/brain-tumor-mri-dataset	✅
MRI	Spine	1,000	1,000	anoukstein/spider-mri-spine-t2-png	✅
US	Breast	750	750	aryashah2k/breast-ultrasound-images-dataset	✅
US	Kidney	750	750	gurjeetkaurmangat/kidney-ultrasound-images-stone-and-no-stone	✅
US	Ovary_Pelvis	750	750	orvile/mmotu-ovarian-ultrasound-images-dataset	✅
XRAY	Chest	1,000	1,000	paultimothymooney/chest-xray-pneumonia	✅
XRAY	Hand	1,000	1,000	antonbudnychuk/hand-xray	✅
XRAY	Knee	1,000	1,000	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.