


Volume 20 No 22 (2022)
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MATHEMATICAL EVALUATION OF COVID-19 DETECTION TECHNIQUE USING CXR RADIOGRAPHS OF PATIENTS, USING DENSENET-201
José Allauca P
Abstract
A current COVID-19 detection tool is CXR imaging, which has been developing since 2019 to provide
early diagnosis; it can be performed in any health unit and is more affordable than Real Time
Polymerase Chain Reaction (RT-PCR) tests. However, diagnosis with Chest X Ray (CXR) images had
not achieved the predictive capacity required to replace the RT-PCR test; previous studies with a
limited number of images have evaluated their models. This research seeks to contribute to the
detection of COVID-19 from CXR images, with the evaluation of a convolutional neural network
model from CXR images, through the use of open source code on a free dataset of approximately 30
thousand images. The algorithm and mathematical model used was DenseNet-201. The results of the
experiment show a precision and accuracy of more than 95% and specificity, sensitivity, predictive
ability and F1 measurement of more than 90%.
Keywords
COVID-19; CNN; CXR; Image Classification.
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