Basu Arpan, Sheikh Khalid Hassan, 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.
Expert Syst Appl. 2022 May 1;193:116377. doi: 10.1016/j.eswa.2021.116377. Epub 2022 Jan 1.
Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It may cause serious ailments in infected individuals and complications may lead to death. X-rays and Computed Tomography (CT) scans can be used for the diagnosis of the disease. In this context, various methods have been proposed for the detection of COVID-19 from radiological images. In this work, we propose an end-to-end framework consisting of deep feature extraction followed by feature selection (FS) for the detection of COVID-19 from CT scan images. For feature extraction, we utilize three deep learning based Convolutional Neural Networks (CNNs). For FS, we use a meta-heuristic optimization algorithm, Harmony Search (HS), combined with a local search method, Adaptive -Hill Climbing (A HC) for better performance. We evaluate the proposed approach on the SARS-COV-2 CT-Scan Dataset consisting of 2482 CT scan images and an updated version of the previous dataset containing 2926 CT scan images. For comparison, we use a few state-of-the-art optimization algorithms. The best accuracy scores obtained by the present approach are 97.30% and 98.87% respectively on the said datasets, which are better than many of the algorithms used for comparison. The performances are also at par with some recent works which use the same datasets. The codes for the FS algorithms are available at: https://github.com/khalid0007/Metaheuristic-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。它可能会在受感染个体中引发严重疾病，并发症可能导致死亡。X射线和计算机断层扫描（CT）可用于该疾病的诊断。在此背景下，已经提出了各种从放射图像中检测COVID-19的方法。在这项工作中，我们提出了一个端到端框架，该框架由深度特征提取和特征选择（FS）组成，用于从CT扫描图像中检测COVID-19。对于特征提取，我们使用了三种基于深度学习的卷积神经网络（CNN）。对于特征选择，我们使用一种元启发式优化算法——和声搜索（HS），并结合一种局部搜索方法——自适应爬山（A HC）以获得更好的性能。我们在由2482张CT扫描图像组成的SARS-CoV-2 CT扫描数据集以及包含2926张CT扫描图像的先前数据集的更新版本上评估所提出的方法。为了进行比较，我们使用了一些最先进的优化算法。本方法在上述数据集上分别获得的最佳准确率分数为97.30%和98.87%，优于许多用于比较的算法。其性能也与一些使用相同数据集的近期研究相当。特征选择算法的代码可在以下网址获取：https://github.com/khalid0007/Metaheuristic-Algorithms 。
