<div class="sc-kdrUpr eZtUed"><div class="sc-UEtKG dGqiYy sc-hDzlxo bEIZRR"><div class="sc-fqwslf gsqkEc"><div class="sc-cBQMlg kAHhUk"><h2 class="sc-dcKlJK sc-cVttbi gqEuPW ksnHgj">About Dataset</h2></div></div></div><div class="sc-fHzVOS cUYeeo"><div class="sc-davvxH nUNNB"><div style="min-height: 80px;"><div class="sc-etVRix bfyesi sc-jCNfQM igJSrG"><p>----------------------UPDATED------------------------UPDATED-------------UPDATED-----------------------<br>

-----------------------------  (3616 COVID-19 Chest X-ray images and lung masks) -------------------------------</p>

<h1><strong>COVID-19 RADIOGRAPHY DATABASE (Winner of the COVID-19 Dataset Award by Kaggle Community)</strong></h1>

<p>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.  </p>

<h3>Please find the link for downloading the whole dataset: <a rel="noreferrer nofollow" aria-label="Data (opens in a new tab)" target="_blank" href="https://drive.google.com/file/d/1bum9Sehb3AzUMHLhBMuowPKyr_PCrB3a/view?usp=sharing">Data</a></h3>

<h4><strong>Our New Dataset</strong></h4>

<p>**COVID-QU-Ex Dataset<br>

**The researchers of Qatar University have compiled the COVID-QU-Ex dataset, which consists of 33,920 chest X-ray (CXR) images including:<br>

11,956 COVID-19, 11,263 Non-COVID infections (Viral or Bacterial Pneumonia), and 10,701 Normal<br>

Ground-truth lung segmentation masks are provided for the entire dataset. This is the largest ever created lung mask dataset.<br>

Besides, 2,913 COVID-19 Infection Segmentation masks are provided from our previous QaTaCov project.<br>

If you would like to download the COVID-QU-Ex dataset, then please check our new Kaggle repository:<br>

<a rel="noreferrer nofollow" aria-label="https://doi.org/10.34740/kaggle/dsv/3122958 (opens in a new tab)" target="_blank" href="https://doi.org/10.34740/kaggle/dsv/3122958">https://doi.org/10.34740/kaggle/dsv/3122958</a></p>

<h3>Please cite the following two articles if you are using this dataset:</h3>

<p>-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. <a rel="noreferrer nofollow" aria-label="Paper link (opens in a new tab)" target="_blank" href="https://ieeexplore.ieee.org/document/9144185">Paper link</a><br>

-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. <a rel="noreferrer nofollow" aria-label="Paper Link (opens in a new tab)" target="_blank" href="https://doi.org/10.1016/j.compbiomed.2021.104319">Paper Link</a><br>

To view images please check image folders and references of each image are provided in the metadata.xlsx.<br>

<strong><em><strong>Research Team members and their affiliation</strong></em></strong><br>

<strong>Muhammad E. H. Chowdhury, PhD</strong> (mchowdhury@qu.edu.qa) <br>

Department of Electrical Engineering, Qatar University, Doha-2713, Qatar<br>

<strong>Tawsifur Rahman</strong> (tawsifurrahman.1426@gmail.com)<br>

Department of Biomedical Physics &amp; Technology, University of Dhaka, Dhaka-1000, Bangladesh<br>

<strong>Amith Khandakar</strong> (amitk@qu.edu.qa) <br>

Department of Electrical Engineering, Qatar University, Doha-2713, Qatar<br>

<strong>Rashid Mazhar, MD</strong><br>

Thoracic Surgery, Hamad General Hospital, Doha-3050, Qatar <br>

<strong>Muhammad Abdul Kadir, PhD</strong><br>

Department of Biomedical Physics &amp; Technology, University of Dhaka, Dhaka-1000, Bangladesh<br>

<strong>Zaid Bin Mahbub, PHD</strong><br>

Department of Mathematics and Physics, North South University, Dhaka-1229, Bangladesh<br>

<strong>Khandakar R. Islam, MD</strong><br>

Department of Orthodontics, Bangabandhu Sheikh Mujib Medical University, Dhaka-1000, Bangladesh<br>

<strong>Muhammad Salman Khan, PhD</strong><br>

Department of Electrical Engineering (JC), University of Engineering and Technology, Peshawar-25120, Pakistan<br>

<strong>Prof. Atif Iqbal, PhD</strong><br>

Department of Electrical Engineering, Qatar University, Doha-2713, Qatar<br>

<strong>Nasser Al-Emadi, PhD</strong><br>

Department of Electrical Engineering, Qatar University, Doha-2713, Qatar<br>

<strong>Prof. Mamun Bin Ibne Reaz. PhD</strong><br>

Department of Electrical, Electronic &amp; Systems Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia<br>

<strong><em><em>Contribution</em></em></strong></p>

<ul>

<li>We have developed the database of COVID-19 x-ray images from the Italian Society of Medical and Interventional Radiology (SIRM) COVID-19 DATABASE [1], Novel Corona Virus 2019 Dataset developed by Joseph Paul Cohen and Paul Morrison, and Lan Dao in GitHub [2] and images extracted from 43 different publications. References of each image are provided in the metadata. Normal and Viral pneumonia images were adopted from the Chest X-Ray Images (pneumonia) database [3].   <br>

<strong>Image Formats</strong></li>

<li>All the images are in Portable Network Graphics (PNG) file format and the resolution are 299*299 pixels.<br>

<strong>Objective</strong></li>

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

<strong>Citation</strong></li>

<li>Please cite these papers if you are using it for any scientific purpose:<br>

-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. <a rel="noreferrer nofollow" aria-label="Paper link (opens in a new tab)" target="_blank" href="https://ieeexplore.ieee.org/document/9144185">Paper link</a><br>

-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. <a rel="noreferrer nofollow" aria-label="Paper Link (opens in a new tab)" target="_blank" href="https://doi.org/10.1016/j.compbiomed.2021.104319">Paper Link</a><br>

<strong>Acknowledgments</strong><br>

Thanks to the  Italian Society of Medical and Interventional Radiology (SIRM) for publicly providing the COVID-19 Chest X-Ray dataset [3], Valencia Region Image Bank (BIMCV) padchest dataset [1]  and would like to thank J. P.  Cohen for taking the initiative to gather images from articles and online resources [5]. Finally to the Chest X-Ray Images (pneumonia) database in Kaggle and Radiological Society of North America (RSNA) Kaggle database for making a wonderful X-ray database for normal, lung opacity, viral, and bacterial pneumonia images [8-9]. Also, a big thanks to our collaborators!<br>

<strong>DATA ACCESS AND USE: Academic/Non-Commercial Use</strong><br>

<strong>References:</strong><br>

[1]<a rel="noreferrer nofollow" aria-label="https://bimcv.cipf.es/bimcv-projects/bimcv-covid19/#1590858128006-9e640421-6711 (opens in a new tab)" target="_blank" href="https://bimcv.cipf.es/bimcv-projects/bimcv-covid19/#1590858128006-9e640421-6711">https://bimcv.cipf.es/bimcv-projects/bimcv-covid19/#1590858128006-9e640421-6711</a><br>

[2]<a rel="noreferrer nofollow" aria-label="https://github.com/ml-workgroup/covid-19-image-repository/tree/master/png (opens in a new tab)" target="_blank" href="https://github.com/ml-workgroup/covid-19-image-repository/tree/master/png">https://github.com/ml-workgroup/covid-19-image-repository/tree/master/png</a><br>

[3]<a rel="noreferrer nofollow" aria-label="https://sirm.org/category/senza-categoria/covid-19/ (opens in a new tab)" target="_blank" href="https://sirm.org/category/senza-categoria/covid-19/">https://sirm.org/category/senza-categoria/covid-19/</a><br>

[4]<a rel="noreferrer nofollow" aria-label="https://eurorad.org (opens in a new tab)" target="_blank" href="https://eurorad.org">https://eurorad.org</a><br>

[5]<a rel="noreferrer nofollow" aria-label="https://github.com/ieee8023/covid-chestxray-dataset (opens in a new tab)" target="_blank" href="https://github.com/ieee8023/covid-chestxray-dataset">https://github.com/ieee8023/covid-chestxray-dataset</a><br>

[6]<a rel="noreferrer nofollow" aria-label="https://figshare.com/articles/COVID-19_Chest_X-Ray_Image_Repository/12580328 (opens in a new tab)" target="_blank" href="https://figshare.com/articles/COVID-19_Chest_X-Ray_Image_Repository/12580328">https://figshare.com/articles/COVID-19_Chest_X-Ray_Image_Repository/12580328</a><br>

[7]<a rel="noreferrer nofollow" aria-label="https://github.com/armiro/COVID-CXNet (opens in a new tab)" target="_blank" href="https://github.com/armiro/COVID-CXNet">https://github.com/armiro/COVID-CXNet</a>  <br>

[8]<a aria-label="https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data (opens in a new tab)" target="_blank" href="https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data">https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data</a><br>

[9] <a aria-label="https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia (opens in a new tab)" target="_blank" href="https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia">https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia</a></li>

</ul></div></div></div>