Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
1K<n<10K
License:
| task_categories: | |
| - image-classification | |
| language: | |
| - en | |
| tags: | |
| - medical | |
| pretty_name: chest-xray | |
| size_categories: | |
| - 1K<n<10K | |
| license: cc | |
| # chest-xray | |
| ## Overview | |
| The chest-xray dataset can be employed as the training and test sets for computer vision tasks like pneumonia classification. | |
| ## Dataset Details | |
| This data originally came from [Chest X-Ray Images (Pneumonia)](https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia) | |
| The original dataset contains : | |
| The dataset is organized into 3 folders (train, test, val) and contains subfolders for each image category (Pneumonia/Normal). | |
| There are 5,856 X-Ray images (JPEG) and 2 categories (Pneumonia/Normal). | |
| ```latex | |
| @article{kermany2018identifying, | |
| title={Identifying medical diagnoses and treatable diseases by image-based deep learning}, | |
| author={Kermany, Daniel S and Goldbaum, Michael and Cai, Wenjia and Valentim, Carolina CS and Liang, Huiying and Baxter, Sally L and McKeown, Alex and Yang, Ge and Wu, Xiaokang and Yan, Fangbing and others}, | |
| journal={cell}, | |
| volume={172}, | |
| number={5}, | |
| pages={1122--1131}, | |
| year={2018}, | |
| publisher={Elsevier} | |
| } | |
| ``` | |
| - Dataset Name: **chest-xray** | |
| - Language: English | |
| - Total Size: 5,856 images | |
| ## Contents | |
| The dataset consists of Chest X-ray images (anterior-posterior), | |
| and 2 class labels: | |
| - "0" normal(27%) | |
| - "1" pneumonia(73%) | |
| ## How to use | |
| ```python | |
| from datasets import load_dataset | |
| dataset = load_dataset("AiresPucrs/chest-xray", split='train') | |
| ``` | |
| ## License | |
| The dataset is licensed under the [Creative Commons License CC0 4.0 International](https://creativecommons.org/licenses/by/4.0/). |