pneumonia
rsna-pneumonia-datasetchest-xray-pneumoniaDataset Summary
The dataset is organized into 3 folders (train, test, val) and contains subfolders for each image category (Pneumonia/Normal). There are 5,863 X-Ray images (JPEG) and 2 categories (Pneumonia/Normal).
Chest X-ray images (anterior-posterior) were selected from retrospective cohorts of pediatric patients of one to five years old from Guangzhou Women and Children’s Medical Center, Guangzhou. All chest X-ray imaging was performed as part of patients’ routine clinical care.
For… See the full description on the dataset page: https://huggingface.co/datasets/hf-vision/chest-xray-pneumonia.Multimodal-Chest-X-ray-dataset-for-Normal-and-Bacterial-Pneumonia-in-Africans
Multimodal Chest X ray dataset for Normal and Bacterial Pneumonia in Africans | Africa (Electric Sheep Africa metadata inventory)
Size category: 1K<n<10K - Formats: imagefolder - Sector: health - Engineered by Electric Sheep Africa
TL;DR
This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.
What… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/Multimodal-Chest-X-ray-dataset-for-Normal-and-Bacterial-Pneumonia-in-Africans.pneumonia-detection-datachest-xray-tb-pneumonia
Chest X-Ray: Tuberculosis, Pneumonia & Normal
A curated chest X-ray image dataset for three-class classification: NORMAL, PNEUMONIA, and TUBERCULOSIS.
Derived from public sources (NIH Chest X-ray Dataset, RSNA Pneumonia Detection Challenge, Kaggle TB datasets) and split into train/validation/test sets.
Dataset Structure
final_dataset/
├── train/ # 9,097 images (NORMAL=3,911 | PNEUMONIA=2,971 | TUBERCULOSIS=2,215)
├── val/ # 1,950 images… See the full description on the dataset page: https://huggingface.co/datasets/realsudarshan/chest-xray-tb-pneumonia.synthetic_pneumoniaThis dataset contains 70,000 synthetic images from the paper "A critical assessment of generative models for synthetic data augmentation on limited pneumonia X-ray data". It consists of synthetically generated X-ray images of bacterial, viral, fungal, and COVID-19 pneumonia, as well as healthy patients with no findings. CAUTION: the images are not real and do not depict real patients. Do not use for medical purposes!