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SZ-CXR

Dataset Information

SZ-CXR is a lung X-ray dataset designed for tuberculosis diagnosis. The dataset contains 336 X-ray images with tuberculosis and 326 X-ray images without tuberculosis. To assist in accurate tuberculosis diagnosis, the dataset also provides lung contour segmentation annotations for each image. The entire dataset is divided into training, validation, and test sets in an 8:1:1 ratio.

The significance of this dataset lies in its application for computer-aided tuberculosis diagnosis using deep learning and segmentation techniques. Although relatively small, with around 662 images, studies have shown that deep convolutional neural networks can achieve statistically reliable tuberculosis prediction results on this dataset through appropriate data augmentation and lung region segmentation processing. This work demonstrates that even on imbalanced and small datasets, optimizing segmentation and data processing methods can improve the accuracy of medical image analysis, providing strong support for disease diagnosis.

Dataset Meta Information

Dimensions Modality Task Type Anatomical Structures Anatomical Area Number of Categories Data Volume File Format
2D X-Ray Segmentation, Classification Tuberculosis Lung 1 566 PNG

Resolution Details

Dataset Statistics size
min 989*1225
median 2937*2743
max 3001*3001

Label Information Statistics

Metric Lung
Case Count 566
Coverage 100%

Visualization

Original X-ray image without tuberculosis and lung annotation:

Original X-ray image containing tuberculosis and lung annotations:

File Structure

SZ-CXR
│
├── images
│   ├── CHNCXR_0001_0.png
│   ├── CHNCXR_0002_0.png
│   ├── CHNCXR_0003_0.png
│   ├── ...
│
└── masks
    ├── CHNCXR_0001_0_mask.png
    ├── CHNCXR_0002_0_mask.png
    ├── CHNCXR_0003_0_mask.png
    ├── ...

Authors and Institutions

Sergii Stirenko (National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute")

Yuriy Kochura (National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute")

Oleg Alienin (National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute")

Oleksandr Rokovyi (National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute")

Yuri Gordienko (National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute")

Source Information

Official Website: https://www.kaggle.com/datasets/raddar/tuberculosis-chest-xrays-shenzhen

Download Link: https://lhncbc.nlm.nih.gov/LHC-downloads/downloads.html#tuberculosis-image-data-sets Article Address: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8477564

Publication Date: 2018

Citation

@inproceedings{stirenko2018chest,
  title={Chest X-ray analysis of tuberculosis by deep learning with segmentation and augmentation},
  author={Stirenko, Sergii and Kochura, Yuriy and Alienin, Oleg and Rokovyi, Oleksandr and Gordienko, Yuri and Gang, Peng and Zeng, Wei},
  booktitle={2018 IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO)},
  pages={422--428},
  year={2018},
  organization={IEEE}
}

Original introduction article is here.