The coronavirus COVID-19 pandemic is today’s major public health crisis, we have faced since the Second World War. The pandemic is spreading around the globe like a wave, and according to the World Health Organization’s recent report, the number of confirmed cases and deaths are rising rapidly. COVID-19 pandemic has created severe social, economic, and political crises, which in turn will leave long-lasting scars. One of the countermeasures against controlling coronavirus outbreak is specific, accurate, reliable, and rapid detection technique to identify infected patients. The availability and affordability of RT-PCR kits remains a major bottleneck in many countries, while handling COVID-19 outbreak effectively. Recent findings indicate that chest radiography anomalies can characterize patients with COVID-19 infection. In this study, Corona-Nidaan, a lightweight deep convolutional neural network (DCNN), is proposed to detect COVID-19, Pneumonia, and Normal cases from chest X-ray image analysis; without any human intervention. We introduce a simple minority class oversampling method for dealing with imbalanced dataset problem. The impact of transfer learning with pre-trained CNNs on chest X-ray based COVID-19 infection detection is also investigated. Experimental analysis shows that Corona-Nidaan model outperforms prior works and other pre-trained CNN based models. The model achieved 95% accuracy for three-class classification with 94% precision and recall for COVID-19 cases. While studying the performance of various pre-trained models, it is also found that VGG19 outperforms other pre-trained CNN models by achieving 93% accuracy with 87% recall and 93% precision for COVID-19 infection detection. The model is evaluated by screening the COVID-19 infected Indian Patient chest X-ray dataset with good accuracy.

冠状病毒 COVID-19 大流行是当今我们自二战以来面临的主要公共卫生危机。疫情正在全球蔓延，根据世界卫生组织最近的报告，确诊病例和死亡人数正在迅速上升。COVID-19 大流行造成了严重的社会、经济和政治危机，这反过来又会留下长期的伤痕。控制冠状病毒爆发的对策之一是特异性、准确、可靠和快速的检测技术，以识别受感染的患者。在有效应对 COVID-19 爆发的同时，RT-PCR 试剂盒的可用性和可负担性仍然是许多国家的主要瓶颈。最近的研究结果表明，胸片异常可以表征 COVID-19 感染患者。在这项研究中，Corona-Nidaan，提出了一种轻量级的深度卷积神经网络 (DCNN)，用于从胸部 X 射线图像分析中检测 COVID-19、肺炎和正常病例；无需任何人为干预。我们介绍了一种简单的少数类过采样方法来处理不平衡数据集问题。还研究了使用预先训练的 CNN 进行迁移学习对基于胸部 X 射线的 COVID-19 感染检测的影响。实验分析表明，Corona-Nidaan 模型优于先前的工作和其他基于 CNN 的预训练模型。该模型对三类分类的准确率达到 95%，对 COVID-19 病例的准确率和召回率为 94%。在研究各种预训练模型的性能时，还发现 VGG19 优于其他预训练的 CNN 模型，实现了 93% 的准确率、87% 的召回率和 93% 的 COVID-19 感染检测准确率。该模型是通过以良好的准确性筛选感染 COVID-19 的印度患者胸部 X 射线数据集来评估的。