---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': NORMAL
          '1': PNEUMONIA
  splits:
  - name: train
    num_bytes: 3186635036.504
    num_examples: 5216
  - name: validation
    num_bytes: 3030633
    num_examples: 16
  - name: test
    num_bytes: 79062317
    num_examples: 624
  download_size: 1230487171
  dataset_size: 3268727986.504
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
license: cc-by-4.0
---

**Dataset 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 the analysis of chest x-ray images, all chest radiographs were initially screened for quality control by removing all low quality or unreadable scans. The diagnoses for the images were then graded by two expert physicians before being cleared for training the AI system. In order to account for any grading errors, the evaluation set was also checked by a third expert.

* Summary taken from [Application of the AI System for Pneumonia Detection Using Chest X-Ray Images](https://www.cell.com/cell/fulltext/S0092-8674(18)30154-5?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0092867418301545%3Fshowall%3Dtrue)

* [Dataset source](https://data.mendeley.com/datasets/rscbjbr9sj/2)

  

**Citation

Citation: Kermany, Daniel; Zhang, Kang; Goldbaum, Michael (2018), “Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification”, Mendeley Data, V2, doi: 10.17632/rscbjbr9sj.2