CovXR: Automated Detection of COVID-19 Pneumonia in Chest X-Rays through Machine Learning
Vishal Shenoy, Sachin B. Malik

TL;DR
CovXR is a convolutional neural network that accurately detects COVID-19 pneumonia from chest X-rays, offering a rapid and reliable alternative to traditional testing methods with over 95% accuracy.
Contribution
This work introduces CovXR, a CNN model trained on over 4,300 X-rays, achieving superior accuracy and speed in COVID-19 detection compared to prior methods.
Findings
Accuracy of 95.5% in COVID-19 detection
F1 score of 0.954 indicating high precision and recall
Sensitivity of 93.5% and specificity of 97.5%
Abstract
Coronavirus disease 2019 (COVID-19) is the highly contagious illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The standard diagnostic testing procedure for COVID-19 is testing a nasopharyngeal swab for SARS-CoV-2 nucleic acid using a real-time polymerase chain reaction (PCR), which can take multiple days to provide a diagnosis. Another widespread form of testing is rapid antigen testing, which has a low sensitivity compared to PCR, but is favored for its quick diagnosis time of usually 15-30 minutes. Patients who test positive for COVID-19 demonstrate diffuse alveolar damage in 87% of cases. Machine learning has proven to have advantages in image classification problems with radiology. In this work, we introduce CovXR as a machine learning model designed to detect COVID-19 pneumonia in chest X-rays (CXR). CovXR is a convolutional neural network (CNN)…
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