Abstract
Background: The recent outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the disease corresponding to it (coronavirus disease 2019; COVID-19) has been declared a pandemic by the World Health Organization. COVID-19 has become a global crisis, shattering health care systems, and weakening economies of most countries. The current methods of testing that are employed include reverse transcription polymerase chain reaction (RT-PCR), rapid antigen testing, and lateral flow testing with RT-PCR being used as the golden standard despite its accuracy being at a mere 63%. It is a manual process which is time consuming, taking about an average of 48 hours to obtain the results. Alternative methods employing deep learning techniques and radiologic images are up and coming. Methods: In this paper, we used a dataset consisting of COVID-19 and non-COVID-19 folders for both X-Ray and CT images which contained a total number of 17,599 images. This dataset has been used to compare 3 (non-pre-trained) CNN models and 5 pre-trained models and their performances in detecting COVID-19 under various parameters like validation accuracy, training accuracy, validation loss, training loss, prediction accuracy, sensitivity and the training time required, with CT and X-Ray images separately. Results: Xception provided the highest validation accuracy (88%) when trained with the dataset containing the X- ray images while VGG19 provided the highest validation accuracy (81.2%) when CT images are used for training. Conclusions: The model, VGG16, showed the most consistent performance, with a validation accuracy of 76.6% for CT images and 87.76% for X-ray images. When comparing the results between the modalities, models trained with the X-ray dataset showed better performances than the same models trained with CT images. Hence, it can be concluded that X-ray images provide a higher accuracy in detecting COVID-19 making it an effective method for detecting COVID-19 in real life.
Highlights
The recent coronavirus disease 2019 (COVID-19) pandemic instigated by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global catastrophe with 257,469,528 registered cases and 5,158,211 deaths registered worldwide as of 23rd November 2021 [WHO Coronavirus (COVID-19) Dashboard, Accessed on 24th November 2021]
The 3 non-pretrained, CNN models were first employed to gain more exposure to how the network functions and how we can alter it to increase accuracy rates
Among Models 1 and 2, it can be noted that Model 2 has a higher training accuracy [79.52% for computerized tomography (CT) images and 84.63% for X-ray images] that is due to the presence of more convolutional layers and increased filter size
Summary
The recent coronavirus disease 2019 (COVID-19) pandemic instigated by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global catastrophe with 257,469,528 registered cases and 5,158,211 deaths registered worldwide as of 23rd November 2021 [WHO Coronavirus (COVID-19) Dashboard, Accessed on 24th November 2021]. Despite being the gold standard of testing, it provides an average accuracy rate of 63%3–5 as the detection of the virus’s genetic material depends on when the patient takes the test and the amount of the virus present in the sample collected. A recently infected patient may receive a negative test result due to smaller amounts of virus present in the sample [Cleveland Clinic, Accessed on 24th November 2021]. The current methods of testing that are employed include reverse transcription polymerase chain reaction (RT-PCR), rapid antigen testing, and lateral flow testing with RT-PCR being used as the golden standard despite its accuracy being at a mere 63% It is a manual process which is time consuming, taking about an average of 48 hours to obtain the results. It article can be found at the end of the article
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