The conventional way of respiratory coronavirus disease 2019 (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, the 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. This learning problem can be considered as one-class classification problem where the target class samples are present and other classes are absent or ill-defined. 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 (target class or class-of-interest (CoI) 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 recent deep learning models, and the experimental results prove that the proposed model outperformed 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.

2019年呼吸道冠状病毒病（COVID-19）的常规诊断方式是逆转录聚合酶链反应（RT-PCR），早期敏感性较低；尤其是如果患者没有症状，可能会进一步引发更严重的肺炎。在此背景下，人们提出了几种深度学习模型，利用公开的胸部 X 光 (CXR) 图像数据集来识别肺部感染，以实现早期诊断、更好的治疗和快速治愈。在这些数据集中，与其他类别（正常、肺炎和结核病）相比，COVID-19 阳性样本数量较少，这给深度学习模型的无偏学习带来了挑战。该学习问题可以被视为一类分类问题，其中目标类样本存在，而其他类不存在或定义不明确。所有深度学习模型都选择类平衡技术来解决这个问题；然而，在任何医疗诊断过程中都应该避免这种情况。此外，深度学习模型也需要大量数据，需要大量计算资源。因此，为了更快地进行诊断，本研究提出了一种基于弹球损失函数的一类支持向量机 (PB-OCSVM)，它可以在有限的 COVID-19 阳性 CXR 样本（目标类或感兴趣类（ CoI）样本）的目标是最大化学习效率并最小化错误预测。将所提出的模型的性能与传统的 OCSVM 和最近的深度学习模型进行了比较，实验结果证明所提出的模型优于最先进的方法。为了验证所提出模型的鲁棒性，还使用噪声 CXR 图像和 UCI 基准数据集进行了实验。