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GeorgeSullivan/
Covid19Radiography
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About Dataset

----------------------UPDATED------------------------UPDATED-------------UPDATED-----------------------
----------------------------- (3616 COVID-19 Chest X-ray images and lung masks) -------------------------------

COVID-19 RADIOGRAPHY DATABASE (Winner of the COVID-19 Dataset Award by Kaggle Community)

A team of researchers from Qatar University, Doha, Qatar, and the University of Dhaka, Bangladesh along with their collaborators from Pakistan and Malaysia in collaboration with medical doctors have created a database of chest X-ray images for COVID-19 positive cases along with Normal and Viral Pneumonia images. This COVID-19, normal, and other lung infection dataset is released in stages. In the first release, we have released 219 COVID-19, 1341 normal, and 1345 viral pneumonia chest X-ray (CXR) images. In the first update, we have increased the COVID-19 class to 1200 CXR images. In the 2nd update, we have increased the database to 3616 COVID-19 positive cases along with 10,192 Normal, 6012 Lung Opacity (Non-COVID lung infection), and 1345 Viral Pneumonia images and corresponding lung masks. We will continue to update this database as soon as we have new x-ray images for COVID-19 pneumonia patients.

Please find the link for downloading the whole dataset: Data

Our New Dataset

**COVID-QU-Ex Dataset
**The researchers of Qatar University have compiled the COVID-QU-Ex dataset, which consists of 33,920 chest X-ray (CXR) images including:
11,956 COVID-19, 11,263 Non-COVID infections (Viral or Bacterial Pneumonia), and 10,701 Normal
Ground-truth lung segmentation masks are provided for the entire dataset. This is the largest ever created lung mask dataset.
Besides, 2,913 COVID-19 Infection Segmentation masks are provided from our previous QaTaCov project.
If you would like to download the COVID-QU-Ex dataset, then please check our new Kaggle repository:
https://doi.org/10.34740/kaggle/dsv/3122958

Please cite the following two articles if you are using this dataset:

-M.E.H. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M.A. Kadir, Z.B. Mahbub, K.R. Islam, M.S. Khan, A. Iqbal, N. Al-Emadi, M.B.I. Reaz, M. T. Islam, “Can AI help in screening Viral and COVID-19 pneumonia?” IEEE Access, Vol. 8, 2020, pp. 132665 - 132676. Paper link
-Rahman, T., Khandakar, A., Qiblawey, Y., Tahir, A., Kiranyaz, S., Kashem, S.B.A., Islam, M.T., Maadeed, S.A., Zughaier, S.M., Khan, M.S. and Chowdhury, M.E., 2020. Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-ray Images. Paper Link
To view images please check image folders and references of each image are provided in the metadata.xlsx.
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
Muhammad Salman Khan, PhD
Department of Electrical Engineering (JC), University of Engineering and Technology, Peshawar-25120, Pakistan
Prof. Atif Iqbal, PhD
Department of Electrical Engineering, Qatar University, Doha-2713, Qatar
Nasser Al-Emadi, PhD
Department of Electrical Engineering, Qatar University, Doha-2713, Qatar
Prof. Mamun Bin Ibne Reaz. PhD
Department of Electrical, Electronic & Systems Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia
Contribution