COVID-19 diagnosis from chest x-rays: developing a simple, fast, and accurate neural network

Vasilis Nikolaou,Sebastiano Massaro,Lampros Stergioulas,Masoud Fakhimi,Wolfgang Garn

doi:10.1007/s13755-021-00166-4

Vasilis Nikolaou, Sebastiano Massaro + Show 3 more

Open Access

https://doi.org/10.1007/s13755-021-00166-4

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Abstract

PurposeChest x-rays are a fast and inexpensive test that may potentially diagnose COVID-19, the disease caused by the novel coronavirus. However, chest imaging is not a first-line test for COVID-19 due to low diagnostic accuracy and confounding with other viral pneumonias. Recent research using deep learning may help overcome this issue as convolutional neural networks (CNNs) have demonstrated high accuracy of COVID-19 diagnosis at an early stage.MethodsWe used the COVID-19 Radiography database [36], which contains x-ray images of COVID-19, other viral pneumonia, and normal lungs. We developed a CNN in which we added a dense layer on top of a pre-trained baseline CNN (EfficientNetB0), and we trained, validated, and tested the model on 15,153 X-ray images. We used data augmentation to avoid overfitting and address class imbalance; we used fine-tuning to improve the model’s performance. From the external test dataset, we calculated the model’s accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score.ResultsOur model differentiated COVID-19 from normal lungs with 95% accuracy, 90% sensitivity, and 97% specificity; it differentiated COVID-19 from other viral pneumonia and normal lungs with 93% accuracy, 94% sensitivity, and 95% specificity.ConclusionsOur parsimonious CNN shows that it is possible to differentiate COVID-19 from other viral pneumonia and normal lungs on x-ray images with high accuracy. Our method may assist clinicians with making more accurate diagnostic decisions and support chest X-rays as a valuable screening tool for the early, rapid diagnosis of COVID-19.

Highlights

COVID-19 is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1]
We utilize the EfficientNet-B0 as our baseline model for the following reasons: (a) it has less parameters than the rest models (B1–B7) of the EfficientNet family, (b) it is more cost-efficient for training and tuning than the more advanced EfficientNetB1-B7 model as it does not require much computational power and (c) it contributes to high accuracy in differentiating COVID-19 from non-COVID-19 viral pneumonia and healthy images, satisfies the rational of our study for developing a parsimonious yet powerful convolution network
Two‐class classification Table 2 reports the performance of the two-class classification model with feature extraction only and with fine-tuning

Summary

Introduction

COVID-19 is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1]. The virus spreads via respiratory droplets or aerosol so that it can be transmitted into individuals’ mouth, nose, or eyes of individuals in close. COVID-19 is usually diagnosed by an RT-PCR test [3] and often is complemented by chest radiographs, including x-ray images and computed tomography (CT) scans [4]. X-ray machines are widely available worldwide and provide images quickly, so chest scans have been recommended, by some researchers [5], for screening during the pandemic. Unlike the RT-PCR test, chest scans provide information about both the status of infection (i.e., presence or absence of the disease) and disease severity. X-ray imaging is an efficient and cost-effective procedure. It requires relatively cheap equipment and can be performed rapidly in isolated rooms with a portable

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