  The novel coronavirus 2019 (COVID-19) is a respiratory syndrome that
resembles pneumonia. The current diagnostic procedure of COVID-19 follows
reverse-transcriptase polymerase chain reaction (RT-PCR) based approach which
however is less sensitive to identify the virus at the initial stage. Hence, a
more robust and alternate diagnosis technique is desirable. Recently, with the
release of publicly available datasets of corona positive patients comprising
of computed tomography (CT) and chest X-ray (CXR) imaging; scientists,
researchers and healthcare experts are contributing for faster and automated
diagnosis of COVID-19 by identifying pulmonary infections using deep learning
approaches to achieve better cure and treatment. These datasets have limited
samples concerned with the positive COVID-19 cases, which raise the challenge
for unbiased learning. Following from this context, this article presents the
random oversampling and weighted class loss function approach for unbiased
fine-tuned learning (transfer learning) in various state-of-the-art deep
learning approaches such as baseline ResNet, Inception-v3, Inception ResNet-v2,
DenseNet169, and NASNetLarge to perform binary classification (as normal and
COVID-19 cases) and also multi-class classification (as COVID-19, pneumonia,
and normal case) of posteroanterior CXR images. Accuracy, precision, recall,
loss, and area under the curve (AUC) are utilized to evaluate the performance
of the models. Considering the experimental results, the performance of each
model is scenario dependent; however, NASNetLarge displayed better scores in
contrast to other architectures, which is further compared with other recently
proposed approaches. This article also added the visual explanation to
illustrate the basis of model classification and perception of COVID-19 in CXR
images.
