Abstract
Chest X-rays have been found to be very promising for assessing COVID-19
patients, especially for resolving emergency-department and
urgent-care-center overcapacity. Deep-learning (DL) methods in
artificial intelligence (AI) play a dominant role as high-performance
classifiers in the detection of the disease using chest X-rays. While
many new DL models have been being developed for this purpose, this
study aimed to investigate the fine tuning of pretrained convolutional
neural networks (CNNs) for the classification of COVID-19 using chest
X-rays. Three pretrained CNNs, which are AlexNet, GoogleNet, and
SqueezeNet, were selected and fine-tuned without data augmentation to
carry out 2-class and 3-class classification tasks using 3 public chest
X-ray databases. In comparison with other recently developed DL models,
the 3 pretrained CNNs achieved very high classification results in terms
of accuracy, sensitivity, specificity, precision, F1 score, and area
under the receiver-operating-characteristic curve. AlexNet, GoogleNet,
and SqueezeNet require the least training time among pretrained DL
models, but with suitable selection of training parameters, excellent
classification results can be achieved without data augmentation by
these networks. The findings contribute to the urgent need for
harnessing the pandemic by facilitating the deployment of AI tools that
are fully automated and readily available in the public domain for rapid
implementation.