Paul Ashis, Basu Arpan, Mahmud Mufti, Kaiser M Shamim, Sarkar Ram
Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032 India.
Department of Computer Science, Nottingham Trent University, Clifton, Nottingham NG11 8NS UK.
Neural Comput Appl. 2022 Jan 5:1-15. doi: 10.1007/s00521-021-06737-6.
Novel Coronavirus 2019 disease or COVID-19 is a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The use of chest X-rays (CXRs) has become an important practice to assist in the diagnosis of COVID-19 as they can be used to detect the abnormalities developed in the infected patients' lungs. With the fast spread of the disease, many researchers across the world are striving to use several deep learning-based systems to identify the COVID-19 from such CXR images. To this end, we propose an inverted bell-curve-based ensemble of deep learning models for the detection of COVID-19 from CXR images. We first use a selection of models pretrained on ImageNet dataset and use the concept of transfer learning to retrain them with CXR datasets. Then the trained models are combined with the proposed inverted bell curve weighted ensemble method, where the output of each classifier is assigned a weight, and the final prediction is done by performing a weighted average of those outputs. We evaluate the proposed method on two publicly available datasets: the COVID-19 Radiography Database and the IEEE COVID Chest X-ray Dataset. The accuracy, F1 score and the AUC ROC achieved by the proposed method are 99.66%, 99.75% and 99.99%, respectively, in the first dataset, and, 99.84%, 99.81% and 99.99%, respectively, in the other dataset. Experimental results ensure that the use of transfer learning-based models and their combination using the proposed ensemble method result in improved predictions of COVID-19 in CXRs.
2019年新型冠状病毒病（COVID-19）是一种由严重急性呼吸综合征冠状病毒2（SARS-CoV-2）引起的病毒性疾病。胸部X光（CXR）检查已成为辅助诊断COVID-19的一项重要手段，因为它可用于检测受感染患者肺部出现的异常情况。随着该疾病的迅速传播，世界各地的许多研究人员都在努力使用多种基于深度学习的系统，从此类CXR图像中识别出COVID-19。为此，我们提出了一种基于倒钟形曲线的深度学习模型集成方法，用于从CXR图像中检测COVID-19。我们首先选用在ImageNet数据集上预训练的模型，并利用迁移学习的概念，使用CXR数据集对其进行重新训练。然后，将训练好的模型与所提出的倒钟形曲线加权集成方法相结合，为每个分类器的输出分配一个权重，并通过对这些输出进行加权平均来完成最终预测。我们在两个公开可用的数据集上对所提出的方法进行了评估：COVID-19放射影像数据库和IEEE COVID胸部X光数据集。在所提出的方法中，第一个数据集的准确率、F1分数和AUC ROC分别为99.66%、99.75%和99.99%，另一个数据集的相应指标分别为99.84%、99.81%和99.99%。实验结果表明，使用基于迁移学习的模型并结合所提出的集成方法，能够提高对CXR图像中COVID-19的预测能力。
