Tuberculosis (TB) Chest X-ray Database

To view images please check image folders and references of each image are provided in the metadata.csv.

Research Team members and their affiliation
Muhammad E. H. Chowdhury, PhD (mchowdhury@qu.edu.qa)
Department of Electrical Engineering, Qatar University, Doha-2713, Qatar
Tawsifur Rahman (tawsifurrahman.1426@gmail.com)
Department of Biomedical Physics & Technology, University of Dhaka, Dhaka-1000, Bangladesh
Amith Khandakar (amitk@qu.edu.qa)
Department of Electrical Engineering, Qatar University, Doha-2713, Qatar
Rashid Mazhar, MD
Thoracic Surgery, Hamad General Hospital, Doha-3050, Qatar
Muhammad Abdul Kadir, PhD
Department of Biomedical Physics & Technology, University of Dhaka, Dhaka-1000, Bangladesh
Zaid Bin Mahbub, PhD
Department of Mathematics and Physics, North South University, Dhaka-1229, Bangladesh
Khandakar R. Islam, MD
Department of Orthodontics, Bangabandhu Sheikh Mujib Medical University, Dhaka-1000, Bangladesh

Contribution

• This dataset contains CXR images of Normal (3500) and patients with TB (3500). The TB database is collected from the source:

1. NLM dataset: National Library of Medicine (NLM) in the U.S. [1] has made two lung X-ray datasets publicly available: the Montgomery and Shenzhen datasets.
2. Belarus dataset: Belarus Set [2] was collected for a drug resistance study initiated by the National Institute of Allergy and Infectious Diseases, Ministry of Health, Republic of Belarus.
3. NIAID TB dataset: NIAID TB portal program dataset [3], which contains about 3000 TB positive CXR images from about 3087 cases.
4. RSNA CXR dataset: RSNA pneumonia detection challenge dataset [4], which is comprised of about 30,000 chest X-ray images, where 10,000 images are normal and others are abnormal and lung opacity images.

This database has been used in the paper titled “Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization” published in IEEE Access in 2020.

Objective

• Researchers can use this database to produce useful and impactful scholarly work on COVID-19, which can help in tackling this pandemic.

Citation

• Please cite this database if you are using it for any scientific purpose:
  Tawsifur Rahman, Amith Khandakar, Muhammad A. Kadir, Khandaker R. Islam, Khandaker F. Islam, Zaid B. Mahbub, Mohamed Arselene Ayari, Muhammad E. H. Chowdhury. "Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization". [Accepted: IEEE Access] [https://arxiv.org/ftp/arxiv/papers/2007/2007.14895.pdf]

References:
[1] S. Jaeger, S. Candemir, S. Antani, Y.-X. J. Wáng, P.-X. Lu, and G. Thoma, "Two public chest X-ray datasets for computer-aided screening of pulmonary diseases," Quantitative imaging in medicine and surgery, vol. 4 (6), p. 475(2014)
[2] B. P. Health. (2020). BELARUS TUBERCULOSIS PORTAL [Online]. Available: http://tuberculosis.by/. [Accessed on 09-June-2020]
3. [Online]. Available: https://data.tbportals.niaid.nih.gov/.
[4] kaggle. RSNA Pneumonia Detection Challenge [Online]. Available: https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data. [Accessed on 09-June-2020]