COVID-19 Radiography Database

----------------------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](https://drive.google.com/file/d/1bum9Sehb3AzUMHLhBMuowPKyr_PCrB3a/view?usp=sharing)

#### **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](https://ieeexplore.ieee.org/document/9144185)
-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](https://doi.org/10.1016/j.compbiomed.2021.104319)

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** ([e-mail])
Department of Electrical Engineering, Qatar University, Doha-2713, Qatar
**Tawsifur Rahman** ([e-mail])
Department of Biomedical Physics & Technology, University of Dhaka, Dhaka-1000, Bangladesh
**Amith Khandakar** ([e-mail])
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****
- 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].

**Image Formats**
- All the images are in Portable Network Graphics (PNG) file format and the resolution are 299*299 pixels.

**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 these papers if you are using it for any scientific purpose:
-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](https://ieeexplore.ieee.org/document/9144185)
-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](https://doi.org/10.1016/j.compbiomed.2021.104319)

**Acknowledgments**
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!
**DATA ACCESS AND USE: Academic/Non-Commercial Use**
**References:**
[1]https://bimcv.cipf.es/bimcv-projects/bimcv-covid19/#1590858128006-9e640421-6711
[2]https://github.com/ml-workgroup/covid-19-image-repository/tree/master/png
[3]https://sirm.org/category/senza-categoria/covid-19/
[4]https://eurorad.org
[5]https://github.com/ieee8023/covid-chestxray-dataset
[6]https://figshare.com/articles/COVID-19_Chest_X-Ray_Image_Repository/12580328
[7]https://github.com/armiro/COVID-CXNet
[8]https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data
[9] https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia

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----------------------------- (3616 例 COVID-19 胸部 X 光片及肺部掩码) -------------------------------

# **COVID-19 放射学数据库 (荣获 Kaggle 社区颁发的 COVID-19 数据集奖项)**
来自卡塔尔大学多哈分校、孟加拉国达卡大学的研究团队，联合巴基斯坦和马来西亚的合作者，以及医学专家共同构建了一个针对 COVID-19 阳性病例的胸部 X 光片数据库，并包含正常及病毒性肺炎图像。该 COVID-19、正常及其他肺部感染数据集分阶段发布。在首次发布中，我们发布了 219 例 COVID-19、1341 例正常和 1345 例病毒性肺炎的胸部 X 光 (CXR) 照片。在首次更新中，我们将 COVID-19 类别的图像数量增加至 1200 张。在第二次更新中，我们将数据库扩展至包含 3616 例 COVID-19 阳性病例、10,192 例正常、6012 例肺部不透明度（非 COVID 肺部感染）和 1345 例病毒性肺炎图像及相应的肺部掩码。一旦我们获得新的 COVID-19 肺炎患者的 X 光片，我们将继续更新此数据库。

### 请点击以下链接下载整个数据集：[数据](https://drive.google.com/file/d/1bum9Sehb3AzUMHLhBMuowPKyr_PCrB3a/view?usp=sharing)

#### **我们的新数据集

**COVID-QU-Ex 数据集
卡塔尔大学的研究人员编制了 COVID-QU-Ex 数据集，该数据集包含 33,920 张胸部 X 光 (CXR) 照片，包括：11,956 例 COVID-19、11,263 例非 COVID 感染（病毒或细菌性肺炎）和 10,701 例正常。
整个数据集提供了地面真实肺部分割掩码。这是迄今为止创建的最大的肺部掩码数据集。
此外，还提供了来自我们之前 QaTaCov 项目的 2,913 例 COVID-19 感染分割掩码。

如果您想下载 COVID-QU-Ex 数据集，请访问我们的新 Kaggle 仓库：
https://doi.org/10.34740/kaggle/dsv/3122958

### 如果您使用此数据集，请引用以下两篇论文：
-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, “AI 能否帮助筛查病毒和 COVID-19 肺炎？” IEEE Access, 第 8 卷，2020 年，第 132665 - 132676 页。[论文链接](https://ieeexplore.ieee.org/document/9144185)
-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. 和 Chowdhury, M.E.，2020 年。探索图像增强技术在 COVID-19 检测中的应用效果。[论文链接](https://doi.org/10.1016/j.compbiomed.2021.104319)

要查看图像，请检查图像文件夹，每张图像的参考资料都包含在 metadata.xlsx 中。

*****研究团队成员及其所属机构*****
**Muhammad E. H. Chowdhury, 博士** ([e-mail])
卡塔尔大学电气工程系，多哈-2713，卡塔尔
**Tawsifur Rahman** ([e-mail])
孟加拉国达卡大学生物医学物理与技术系，达卡-1000，孟加拉国
**Amith Khandakar** ([e-mail])
卡塔尔大学电气工程系，多哈-2713，卡塔尔
**Rashid Mazhar, 医学博士**
胸腔外科，哈马德总医院，多哈-3050，卡塔尔
**Muhammad Abdul Kadir, 博士**
孟加拉国达卡大学生物医学物理与技术系，达卡-1000，孟加拉国
**Zaid Bin Mahbub, 哲学博士**
北南大学数学与物理系，达卡-1229，孟加拉国
**Khandakar R. Islam, 医学博士**
孟加拉国达卡大学邦加班杜·谢赫·穆吉布医学大学正畸科，达卡-1000，孟加拉国
**Muhammad Salman Khan, 哲学博士**
巴基斯坦工程与科技大学电气工程系（JC），白沙瓦-25120，巴基斯坦
**Prof. Atif Iqbal, 哲学博士**
卡塔尔大学电气工程系，多哈-2713，卡塔尔
**Nasser Al-Emadi, 哲学博士**
卡塔尔大学电气工程系，多哈-2713，卡塔尔
**Prof. Mamun Bin Ibne Reaz. 哲学博士**
马来西亚国民大学电气、电子与系统工程系，雪兰莪州班加罗尔 43600，马来西亚

****贡献****
- 我们从意大利医学和介入放射学学会 (SIRM) 的 COVID-19 数据库 [1]、Joseph Paul Cohen 和 Paul Morrison 开发的 2019 年新型冠状病毒数据集以及 GitHub 上的 Lan Dao [2] 以及从 43 篇不同出版物中提取的图像中开发了 COVID-19 X 光图像数据库。每张图像的参考文献都包含在元数据中。正常和病毒性肺炎图像来自 Chest X-Ray Images (pneumonia) 数据库 [3]。

**图像格式**
- 所有图像均为便携式网络图形 (PNG) 文件格式，分辨率为 299*299 像素。

**目标**
- 研究人员可以使用此数据库产生具有实用性和影响力的学术成果，以应对 COVID-19 大流行。

**引用**
- 如果您使用此数据集进行任何科学研究，请引用以下论文：
-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, “AI 能否帮助筛查病毒和 COVID-19 肺炎？” IEEE Access, 第 8 卷，2020 年，第 132665 - 132676 页。[论文链接](https://ieeexplore.ieee.org/document/9144185)
-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. 和 Chowdhury, M.E.，2020 年。探索图像增强技术在 COVID-19 检测中的应用效果。[论文链接](https://doi.org/10.1016/j.compbiomed.2021.104319)

**致谢**
感谢意大利医学和介入放射学学会 (SIRM) 公开提供 COVID-19 胸部 X 光数据集 [3]、瓦伦西亚地区图像库 (BIMCV) padchest 数据集 [1] 以及感谢 J. P. Cohen 主动收集文章和在线资源中的图像 [5]。最后，感谢 Chest X-Ray Images (pneumonia) 数据库在 Kaggle 和北美放射学会 (RSNA) Kaggle 数据库中制作了出色的正常、肺部不透明度、病毒性和细菌性肺炎图像数据库 [8-9]。还要感谢我们的合作者！
**数据访问和使用：学术/非商业用途**
**参考文献**
[1]https://bimcv.cipf.es/bimcv-projects/bimcv-covid19/#1590858128006-9e640421-6711
[2]https://github.com/ml-workgroup/covid-19-image-repository/tree/master/png
[3]https://sirm.org/category/senza-categoria/covid-19/
[4]https://eurorad.org
[5]https://github.com/ieee8023/covid-chestxray-dataset
[6]https://figshare.com/articles/COVID-19_Chest_X-Ray_Image_Repository/12580328
[7]https://github.com/armiro/COVID-CXNet
[8]https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data
[9] https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia