Tuberculosis (TB) Chest X-ray Database

Citation Author(s):
Tawsifur
Rahman
Dhaka University
Amith
Khandakar
Department of Electrical Engineering, Qatar University
Muhammad Enamul Hoque
Chowdhury
Department of Electrical Engineering, Qatar University
Submitted by:
Amith khandakar
Last updated:
Tue, 10/20/2020 - 11:18
DOI:
10.21227/mps8-kb56
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Abstract 

A team of researchers from Qatar University, Doha, Qatar, and the University of Dhaka, Bangladesh along with their collaborators from Malaysia in collaboration with medical doctors from Hamad Medical Corporation and Bangladesh have created a database of chest X-ray images for Tuberculosis (TB) positive cases along with Normal images. In our current release, there are 3500 TB images, and 3500 normal images.

Note: The research team managed to classify TB and Normal Chest X-ray images with an accuracy of 98.3%. This scholarly work is published in IEEE Access Link. Please make sure you give credit to us while using the dataset, code, and trained models.

Credit should go to the following: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]

Instructions: 

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

Research Team members and their affiliationMuhammad E. H. Chowdhury, PhD (mchowdhury@qu.edu.qa)Department of Electrical Engineering, Qatar University, Doha-2713, QatarTawsifur Rahman (tawsifurrahman.1426@gmail.com)Department of Biomedical Physics & Technology, University of Dhaka, Dhaka-1000, BangladeshAmith Khandakar (amitk@qu.edu.qa)Department of Electrical Engineering, Qatar University, Doha-2713, QatarRashid Mazhar, MDThoracic Surgery, Hamad General Hospital, Doha-3050, QatarMuhammad Abdul Kadir, PhDDepartment of Biomedical Physics & Technology, University of Dhaka, Dhaka-1000, BangladeshZaid Bin Mahbub, PhDDepartment of Mathematics and Physics, North South University, Dhaka-1229, BangladeshKhandakar R. Islam, MDDepartment 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]