Abstract
Chest radiographs (X-rays) combined with Deep Convolutional Neural Network (CNN) methods have been demonstrated to detect and diagnose the onset of COVID-19, the disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). However, questions remain regarding the accuracy of those methods as they are often challenged by limited datasets, performance legitimacy on imbalanced data, and have their results typically reported without proper confidence intervals. Considering the opportunity to address these issues, in this study, we propose and test six modified deep learning models, including VGG16, InceptionResNetV2, ResNet50, MobileNetV2, ResNet101, and VGG19 to detect SARS-CoV-2 infection from chest X-ray images. Results are evaluated in terms of accuracy, precision, recall, and f- score using a small and balanced dataset (Study One), and a larger and imbalanced dataset (Study Two). With 95% confidence interval, VGG16 and MobileNetV2 show that, on both datasets, the model could identify patients with COVID-19 symptoms with an accuracy of up to 100%. We also present a pilot test of VGG16 models on a multi-class dataset, showing promising results by achieving 91% accuracy in detecting COVID-19, normal, and Pneumonia patients. Furthermore, we demonstrated that poorly performing models in Study One (ResNet50 and ResNet101) had their accuracy rise from 70% to 93% once trained with the comparatively larger dataset of Study Two. Still, models like InceptionResNetV2 and VGG19’s demonstrated an accuracy of 97% on both datasets, which posits the effectiveness of our proposed methods, ultimately presenting a reasonable and accessible alternative to identify patients with COVID-19.
Highlights
The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), previously known as the Novel Coronavirus, was first reported in Wuhan, China and rapidly spread around the world, pushing the World Health Organization (WHO) to declare the outbreak of the virus as a global pandemic and health emergency on March 11, 2020
RESEARCH METHODOLOGY We propose three separate studies, wherein three distinct datasets were used, as detailed below: 1) Study One – smaller, balanced dataset: chest X-ray images of 25 patients with COVID-19 symptoms, and 25 images of patients with diagnosed pneumonia, obtained from the open-source repository shared by Dr Joseph Cohen [43]
In Study Two, some of the best models we acquired were VGG16, VGG19, InceptionResNetV2, and MobileNetV2 and accuracy lies between 97% to around 100%
Summary
The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), previously known as the Novel Coronavirus, was first reported in Wuhan, China and rapidly spread around the world, pushing the World Health Organization (WHO) to declare the outbreak of the virus as a global pandemic and health emergency on March 11, 2020. 19 million people have been infected worldwide, with the number of deaths surpassing 700, 000, and 12 million. In the United States, the first case was reported on January 20, 2020, which evolved into a current number of confirmed cases, deaths, and recovered patients reaching more than 5 million, 162, 000, and 2.5 million, respectively (August 6, 2020 data) [1]. The virus can spread quickly among humans via community transmission, such as close contact between individuals, and the transfer of respiratory droplets produced via coughing, sneezing, and talking.
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