TY - JOUR
T1 - On the use of deep learning for imaging-based COVID-19 detection using chest X-rays
AU - Okolo, Gabriel Iluebe
AU - Katsigiannis, Stamos
AU - Althobaiti, Turke
AU - Ramzan, Naeem
PY - 2021/8/24
Y1 - 2021/8/24
N2 - The global COVID-19 pandemic that started in 2019 and created major disruptions around the world demonstrated the imperative need for quick, inexpensive, accessible and reliable diagnostic methods that would allow the detection of infected individuals with minimal resources. Radiography, and more specifically, chest radiography, is a relatively inexpensive medical imaging modality that can potentially offer a solution for the diagnosis of COVID-19 cases. In this work, we examined eleven deep convolutional neural network architectures for the task of classifying chest X-ray images as belonging to healthy individuals, individuals with COVID-19 or individuals with viral pneumonia. All the examined networks are established architectures that have been proven to be efficient in image classification tasks, and we evaluated three different adjustments to modify the architectures for the task at hand by expanding them with additional layers. The proposed approaches were evaluated for all the examined architectures on a dataset with real chest X-ray images, reaching the highest classification accuracy of 98.04% and the highest F1-score of 98.22% for the best-performing setting.
AB - The global COVID-19 pandemic that started in 2019 and created major disruptions around the world demonstrated the imperative need for quick, inexpensive, accessible and reliable diagnostic methods that would allow the detection of infected individuals with minimal resources. Radiography, and more specifically, chest radiography, is a relatively inexpensive medical imaging modality that can potentially offer a solution for the diagnosis of COVID-19 cases. In this work, we examined eleven deep convolutional neural network architectures for the task of classifying chest X-ray images as belonging to healthy individuals, individuals with COVID-19 or individuals with viral pneumonia. All the examined networks are established architectures that have been proven to be efficient in image classification tasks, and we evaluated three different adjustments to modify the architectures for the task at hand by expanding them with additional layers. The proposed approaches were evaluated for all the examined architectures on a dataset with real chest X-ray images, reaching the highest classification accuracy of 98.04% and the highest F1-score of 98.22% for the best-performing setting.
KW - chest X-ray
KW - CNN
KW - COVID-19
KW - deep learning
KW - image classification
UR - http://www.scopus.com/inward/record.url?scp=85113711316&partnerID=8YFLogxK
U2 - 10.3390/s21175702
DO - 10.3390/s21175702
M3 - Article
C2 - 34502591
AN - SCOPUS:85113711316
SN - 1424-8220
VL - 21
JO - Sensors
JF - Sensors
IS - 17
M1 - 5702
ER -