Volume 20 No 22 (2022)
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Mathematical evaluation of COVID-19 detection technique using CXR radiographs of patients, using CNN vggnet-16
José Allauca P
The detection of COVID-19 by CXR imaging is a support tool for physicians and specialists since the pandemic and has been evolving rapidly because it provides early diagnosis, can be performed in any health center, and is more affordable than Real-Time Polymerase Chain Reaction (RT-PCR) tests. However, Chest X-Ray (CXR) imaging had not achieved the predictive capacity needed to replace the RT-PCR test; previous studies have evaluated their models with a limited amount of images. This study aims to contribute to the evaluation of a convolutional neural network (CNN) model to detect COVID-19 from CXR images, using open source and a free dataset containing approximately 30,000 images. The mathematical model or algorithm used was VGGNet-16. The results of the experiments show accuracy and precision of more than 95% and sensitivity, specificity, F1-measure,andthedictive ability of more than 90%
CNN, COVID-19, CXR, Image Classification.
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