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The infection of respiratory coronavirus disease 2019 (COVID-19) starts withthe upper respiratory tract and as the virus grows, the infection can progressto lungs and develop pneumonia. The conventional way of COVID-19 diagnosis isreverse transcription polymerase chain reaction (RT-PCR), which is lesssensitive during early stages; especially if the patient is asymptomatic, whichmay further cause more severe pneumonia. In this context, several deep learningmodels have been proposed to identify pulmonary infections using publiclyavailable chest X-ray (CXR) image datasets for early diagnosis, bettertreatment and quick cure. In these datasets, presence of less number ofCOVID-19 positive samples compared to other classes (normal, pneumonia andTuberculosis) raises the challenge for unbiased learning of deep learningmodels. All deep learning models opted class balancing techniques to solve thisissue; which however should be avoided in any medical diagnosis process.Moreover, the deep learning models are also data hungry and need massivecomputation resources. Therefore for quicker diagnosis, this research proposesa novel pinball loss function based one-class support vector machine(PB-OCSVM), that can work in presence of limited COVID-19 positive CXR sampleswith objectives to maximize the learning efficiency and to minimize the falsepredictions. The performance of the proposed model is compared withconventional OCSVM and existing deep learning models, and the experimentalresults prove that the proposed model outperformed over state-of-the-artmethods. To validate the robustness of the proposed model, experiments are alsoperformed with noisy CXR images and UCI benchmark datasets.

Pinball-OCSVM for early-stage COVID-19 diagnosis with limited posteroanterior chest X-ray images