Bandyopadhyay Rajarshi, Basu Arpan, Cuevas Erik, Sarkar Ram
Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India.
Departamento de Electrónica, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jal, Mexico.
Appl Soft Comput. 2021 Nov;111:107698. doi: 10.1016/j.asoc.2021.107698. Epub 2021 Jul 14.
Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It may cause severe ailments in infected individuals. The more severe cases may lead to death. Automated methods which can detect COVID-19 in radiological images can help in the screening of patients. In this work, a two-stage pipeline composed of feature extraction followed by feature selection (FS) for the detection of COVID-19 from CT scan images is proposed. For feature extraction, a state-of-the-art Convolutional Neural Network (CNN) model based on the DenseNet architecture is utilised. To eliminate the non-informative and redundant features, the meta-heuristic called Harris Hawks optimisation (HHO) algorithm combined with Simulated Annealing (SA) and Chaotic initialisation is employed. The proposed approach is evaluated on the SARS-COV-2 CT-Scan dataset which consists of 2482 CT-scans. Without the Chaotic initialisation and the SA, the method gives an accuracy of around 98.42% which further increases to 98.85% on the inclusion of the two and thus delivers better performance than many state-of-the-art methods and various meta-heuristic based FS algorithms. Also, comparison has been drawn with many hybrid variants of meta-heuristic algorithms. Although HHO falls behind a few of the hybrid variants, when Chaotic initialisation and SA are incorporated into it, the proposed algorithm performs better than any other algorithm with which comparison has been drawn. The proposed algorithm decreases the number of features selected by around 75% , which is better than most of the other algorithms.
2019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）引起的传染病。它可能导致感染者出现严重疾病。更严重的病例可能会导致死亡。能够在放射图像中检测COVID-19的自动化方法有助于患者的筛查。在这项工作中，提出了一种两阶段的流程，包括特征提取，然后是用于从CT扫描图像中检测COVID-19的特征选择（FS）。对于特征提取，使用了基于密集连接网络（DenseNet）架构的先进卷积神经网络（CNN）模型。为了消除无信息和冗余特征，采用了结合模拟退火（SA）和混沌初始化的元启发式算法——哈里斯鹰优化（HHO）算法。所提出的方法在包含2482次CT扫描的SARS-COV-2 CT扫描数据集上进行了评估。在没有混沌初始化和SA的情况下，该方法的准确率约为98.42%，在加入这两者后进一步提高到98.85%，因此比许多先进方法和各种基于元启发式的FS算法具有更好的性能。此外，还与许多元启发式算法的混合变体进行了比较。虽然HHO落后于一些混合变体，但当将混沌初始化和SA纳入其中时，所提出的算法比与之比较的任何其他算法表现都更好。所提出的算法将所选特征的数量减少了约75%，这比大多数其他算法都要好。
