  The infection of respiratory coronavirus disease 2019 (COVID-19) starts with
the upper respiratory tract and as the virus grows, the infection can progress
to lungs and develop pneumonia. The conventional way of COVID-19 diagnosis is
reverse transcription polymerase chain reaction (RT-PCR), which is less
sensitive during early stages; especially if the patient is asymptomatic, which
may further cause more severe pneumonia. In this context, several deep learning
models have been proposed to identify pulmonary infections using publicly
available chest X-ray (CXR) image datasets for early diagnosis, better
treatment and quick cure. In these datasets, presence of less number of
COVID-19 positive samples compared to other classes (normal, pneumonia and
Tuberculosis) raises the challenge for unbiased learning of deep learning
models. All deep learning models opted class balancing techniques to solve this
issue; which however should be avoided in any medical diagnosis process.
Moreover, the deep learning models are also data hungry and need massive
computation resources. Therefore for quicker diagnosis, this research proposes
a novel pinball loss function based one-class support vector machine
(PB-OCSVM), that can work in presence of limited COVID-19 positive CXR samples
with objectives to maximize the learning efficiency and to minimize the false
predictions. The performance of the proposed model is compared with
conventional OCSVM and existing deep learning models, and the experimental
results prove that the proposed model outperformed over state-of-the-art
methods. To validate the robustness of the proposed model, experiments are also
performed with noisy CXR images and UCI benchmark datasets.
