Banerjee Avinandan, Sarkar Arya, Roy Sayantan, Singh Pawan Kumar, Sarkar Ram
Department of Information Technology, Jadavpur University, Jadavpur University Second Campus, Plot No. 8, Salt Lake Bypass, LB Block, Sector III, Salt Lake City, Kolkata 700106, West Bengal, India.
Department of Computer Science, University of Engineering and Management, University Area, Plot No. III - B/5, New Town, Action Area - III, Kolkata 700160, West Bengal, India.
Biomed Signal Process Control. 2022 Sep;78:104000. doi: 10.1016/j.bspc.2022.104000. Epub 2022 Jul 15.
The novel COVID-19 pandemic, has effectively turned out to be one of the deadliest events in modern history, with unprecedented loss of human life, major economic and financial setbacks and has set the entire world back quite a few decades. However, detection of the COVID-19 virus has become increasingly difficult due to the mutating nature of the virus, and the rise in asymptomatic cases. To counteract this and contribute to the research efforts for a more accurate screening of COVID-19, we have planned this work. Here, we have proposed an ensemble methodology for deep learning models to solve the task of COVID-19 detection from chest X-rays (CXRs) to assist Computer-Aided Detection (CADe) for medical practitioners. We leverage the strategy of transfer learning for Convolutional Neural Networks (CNNs), widely adopted in recent literature, and further propose an efficient ensemble network for their combination. The DenseNet-201 architecture has been trained only once to generate multiple snapshots, offering diverse information about the extracted features from CXRs. We follow the strategy of decision-level fusion to combine the decision scores using the blending algorithm through a Random Forest (RF) meta-learner. Experimental results confirm the efficacy of the proposed ensemble method, as shown through impressive results upon two open access COVID-19 CXR datasets - the largest COVID-X dataset, as well as a smaller scale dataset. On the large COVID-X dataset, the proposed model has achieved an accuracy score of 94.55% and on the smaller dataset by Chowdhury et al., the proposed model has achieved a 98.13% accuracy score.
新型冠状病毒肺炎大流行实际上已成为现代历史上最致命的事件之一，造成了前所未有的人员生命损失、重大经济和金融挫折，使整个世界倒退了几十年。然而，由于病毒的变异性质以及无症状病例的增加，新冠病毒的检测变得越来越困难。为了应对这一情况并助力更准确筛查新冠病毒的研究工作，我们规划了这项研究。在此，我们提出了一种深度学习模型的集成方法，以解决从胸部X光片（CXR）检测新冠病毒的任务，辅助医学从业者进行计算机辅助检测（CADe）。我们利用了近期文献中广泛采用的卷积神经网络（CNN）迁移学习策略，并进一步提出了一种高效的集成网络用于其组合。DenseNet - 201架构仅训练一次以生成多个快照，提供有关从胸部X光片中提取特征的多样信息。我们采用决策级融合策略，通过随机森林（RF）元学习器使用混合算法组合决策分数。实验结果证实了所提出的集成方法的有效性，这在两个公开获取的新冠病毒胸部X光片数据集——最大的COVID - X数据集以及一个较小规模的数据集上取得了令人瞩目的结果。在大型COVID - X数据集上，所提出的模型准确率达到了94.55%，在Chowdhury等人的较小数据集上，所提出的模型准确率达到了98.13%。
