gurnameh-99 / covid-xray-classifier

COVID 19 XRAY CLASSIFIER

• In this repository, a model for classifying the cases of COVID-19 has been provided.
• A descriptive EDA of the dataset has been provided.
• The model has been implemented in Python using tensorflow.
• This is a deep neural network model that uses a convolutional neural network to classify the cases of COVID-19.

Classes

Results

*****COVID-19 CHEST X-RAY DATABASE

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. We will continue to update this database as soon as we have new x-ray images for COVID-19 pneumonia patients.

**COVID-19 data:

COVID data are collected from different publicly accessible dataset, online sources and published papers. -2473 CXR images are collected from padchest dataset[1]. -183 CXR images from a Germany medical school[2]. -559 CXR image from SIRM, Github, Kaggle & Tweeter[3,4,5,6] -400 CXR images from another Github source[7].

***Normal images:

10192 Normal data are collected from from three different dataset. -8851 RSNA [8] -1341 Kaggle [9]

***Lung opacity images:

6012 Lung opacity CXR images are collected from Radiological Society of North America (RSNA) CXR dataset [8]

***Viral Pneumonia images:

1345 Viral Pneumonia data are collected from the Chest X-Ray Images (pneumonia) database [9]

Please cite the follwoing 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. -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. arXiv preprint arXiv:2012.02238.

**Reference: [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

**Formats - All the images are in Portable Network Graphics (PNG) file format and resolution are 299299 pixels.

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