COVID-19 Data Analysis using Chest X-Ray
Open Access
- 10 August 2021
- journal article
- Published by Lattice Science Publication (LSP) in International Journal of Advanced Medical Sciences and Technology
- Vol. 1 (4), 5-10
- https://doi.org/10.54105/ijamst.c3018.081421
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
This publication has 14 references indexed in Scilit:
- Temporal Changes of CT Findings in 90 Patients with COVID-19 Pneumonia: A Longitudinal StudyRadiology, 2020
- Radiology Department Preparedness for COVID-19: Radiology Scientific Expert Review PanelRadiology, 2020
- Coronavirus Disease 2019 (COVID-19): A Systematic Review of Imaging Findings in 919 PatientsAmerican Journal of Roentgenology, 2020
- Implementation LSTM Algorithm for Cervical Cancer using Colposcopy DataPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2020
- Identification of a novel coronavirus causing severe pneumonia in human: a descriptive studyChinese Medical Journal, 2020
- Machine Learning for the prediction of the dynamic behavior of a small scale ORC systemEnergy, 2019
- SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image SegmentationNeuroinformatics, 2018
- Deep Residual Learning for Image RecognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?IEEE Transactions on Medical Imaging, 2016
- The Art of Data AugmentationJournal of Computational and Graphical Statistics, 2001