Chakraborty Mainak, Dhavale Sunita Vikrant, Ingole Jitendra
Defence Institute of Advanced Technology (DIAT), Girinagar Pune, 411025 India.
Smt. Kashibai Navale Medical College and General Hospital, Narhe Pune, 411041 India.
Appl Intell (Dordr). 2021;51(5):3026-3043. doi: 10.1007/s10489-020-01978-9. Epub 2021 Feb 2.
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.
新型冠状病毒COVID-19大流行是自第二次世界大战以来我们面临的当今主要公共卫生危机。这场大流行正像波浪一样在全球蔓延，根据世界卫生组织最近的报告，确诊病例数和死亡人数正在迅速上升。COVID-19大流行引发了严重的社会、经济和政治危机，反过来也将留下持久的创伤。控制冠状病毒爆发的对策之一是采用特定、准确、可靠和快速的检测技术来识别感染患者。在有效应对COVID-19爆发的同时，逆转录聚合酶链反应（RT-PCR）试剂盒的可获得性和可承受性在许多国家仍然是一个主要瓶颈。最近的研究结果表明，胸部X光检查异常可作为COVID-19感染患者的特征。在本研究中，提出了一种轻量级深度卷积神经网络（DCNN）“Corona-Nidaan”，用于从胸部X光图像分析中检测COVID-19、肺炎和正常病例；无需任何人工干预。我们引入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。还研究了使用预训练的卷积神经网络（CNN）进行迁移学习对基于胸部X光的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预训练CNN的模型。该模型在三类分类中达到了95%的准确率，对COVID-19病例的精确率和召回率为94%。在研究各种预训练模型的性能时，还发现VGG19在COVID-19感染检测中表现优于其他预训练的CNN模型，准确率达到93%，召回率为87%，精确率为93%。该模型通过筛查COVID-19感染的印度患者胸部X光数据集进行评估，准确率良好。
