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The novel coronavirus 2019 (COVID-19) is a respiratory syndrome thatresembles pneumonia. The current diagnostic procedure of COVID-19 followsreverse-transcriptase polymerase chain reaction (RT-PCR) based approach whichhowever is less sensitive to identify the virus at the initial stage. Hence, amore robust and alternate diagnosis technique is desirable. Recently, with therelease of publicly available datasets of corona positive patients comprisingof computed tomography (CT) and chest X-ray (CXR) imaging; scientists,researchers and healthcare experts are contributing for faster and automateddiagnosis of COVID-19 by identifying pulmonary infections using deep learningapproaches to achieve better cure and treatment. These datasets have limitedsamples concerned with the positive COVID-19 cases, which raise the challengefor unbiased learning. Following from this context, this article presents therandom oversampling and weighted class loss function approach for unbiasedfine-tuned learning (transfer learning) in various state-of-the-art deeplearning approaches such as baseline ResNet, Inception-v3, Inception ResNet-v2,DenseNet169, and NASNetLarge to perform binary classification (as normal andCOVID-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 performanceof the models. Considering the experimental results, the performance of eachmodel is scenario dependent; however, NASNetLarge displayed better scores incontrast to other architectures, which is further compared with other recentlyproposed approaches. This article also added the visual explanation toillustrate the basis of model classification and perception of COVID-19 in CXRimages.

Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks