COVID-19 Data Analysis using Chest X-Ray
Ishtiaque Ahmed*1, Manan Darda2, Neha Tikyani3, Rachit Agrawal4, Manjusha Joshi5 

1Ishtiaque Ahmed*, Student, SVKM‘s Narsee Monjee Institute of Management Studies (NMIMS), Mumbai (Maharashtra), India.
2Manan Darda, Student, SVKM‘s Narsee Monjee Institute of Management Studies (NMIMS), Mumbai (Maharashtra), India.
3Neha Tikyani, Student, SVKM‘s Narsee Monjee Institute of Management Studies (NMIMS), Mumbai (Maharashtra), India.
4Rachit Agrawal, Student, SVKM‘s Narsee Monjee Institute of Management Studies (NMIMS), Mumbai (Maharashtra), India.
5Dr. Manjusha Joshi, Assistant Professor, SVKM‘s Narsee Monjee Institute of Management Studies (NMIMS), Mumbai (Maharashtra), India.
Manuscript received on July 02 2021 | Revised Manuscript received on July 16, 2021 | Manuscript published on August, 10 2021 | PP: 5-10 | Volume-1 Issue-4 August 2021 | Retrieval Number:100.1/ijamst.C3018061321 | DOI:10.54105/ijamst.C3018.081421
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© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The COVID-19 pandemic has caused large-scale outbreaks in more than 150 countries worldwide, causing massive damage to the livelihood of many people. The capacity to identify contaminated patients early and get unique treatment is quite possibly the primary stride in the battle against COVID-19. One of the quickest ways to diagnose patients is to use radiography and radiology images to detect the disease. Early studies have shown that chest X-rays of patients infected with COVID-19 have unique abnormalities. To identify COVID-19 patients from chest X-ray images, we used various deep learning models based on previous studies. We first compiled a data set of 2,815 chest radiographs from public sources. The model produces reliable and stable results with an accuracy of 91.6%, a Positive Predictive Value of 80%, a Negative Predictive Value of 100%, specificity of 87.50%, and Sensitivity of 100%. It is observed that the CNN-based architecture can diagnose COVID-19 disease. The parameters’ outcomes can be further improved by increasing the dataset size and by developing the CNN-based architecture for training the model.
Keywords: COVID-19, CNN, Chest X-ray, ResNet18, Radiograph Images, Deep Learning
Scope of the Article: Chest X-ray