Coronavirus disease (COVID-19) has infected over more than 28.3 million people around the globe and killed 913K people worldwide as on 11 September 2020. With this pandemic, to combat the spreading of COVID-19, effective testing methodologies and immediate medical treatments are much required. Chest X-rays are the widely available modalities for immediate diagnosis of COVID-19. Hence, automation of detection of COVID-19 from chest X-ray images using machine learning approaches is of greater demand. A model for detecting COVID-19 from chest X-ray images is proposed in this paper. A novel concept of cluster-based one-shot learning is introduced in this work. The introduced concept has an advantage of learning from a few samples against learning from many samples in case of deep leaning architectures. The proposed model is a multi-class classification model as it classifies images of four classes, viz., pneumonia bacterial, pneumonia virus, normal, and COVID-19. The proposed model is based on ensemble of Generalized Regression Neural Network (GRNN) and Probabilistic Neural Network (PNN) classifiers at decision level. The effectiveness of the proposed model has been demonstrated through extensive experimentation on a publicly available dataset consisting of 306 images. The proposed cluster-based one-shot learning has been found to be more effective on GRNN and PNN ensembled model to distinguish COVID-19 images from that of the other three classes. It has also been experimentally observed that the model has a superior performance over contemporary deep learning architectures. The concept of one-shot cluster-based learning is being first of its kind in literature, expected to open up several new dimensions in the field of machine learning which require further researching for various applications.

截至 2020 年 9 月 11 日，冠状病毒病 (COVID-19) 已在全球范围内感染了超过 2830 万人，并导致 91.3 万人死亡。在这次大流行中，为了遏制 COVID-19 的传播，需要有效的检测方法和立即的医疗治疗。非常需要。胸部 X 光检查是立即诊断 COVID-19 的广泛使用的方法。因此，使用机器学习方法从胸部 X 射线图像中自动检测 COVID-19 的需求更大。本文提出了一种从胸部 X 射线图像检测 COVID-19 的模型。这项工作引入了一种基于集群的一次性学习的新概念。所引入的概念的优点是从少数样本中学习，而不是在深度学习架构的情况下从许多样本中学习。所提出的模型是一个多类分类模型，因为它对四类图像进行分类，即肺炎细菌、肺炎病毒、正常和 COVID-19。所提出的模型基于决策级别的广义回归神经网络（GRNN）和概率神经网络（PNN）分类器的集成。通过对由 306 张图像组成的公开数据集进行的广泛实验，证明了所提出模型的有效性。研究发现，所提出的基于集群的一次性学习在 GRNN 和 PNN 集成模型上更有效地将 COVID-19 图像与其他三类图像区分开来。实验还观察到该模型比当代深度学习架构具有更优越的性能。 基于集群的一次性学习的概念在文献中尚属首次，预计将在机器学习领域开辟几个新的维度，需要针对各种应用进行进一步的研究。