Faculty of Science, Zagazig University, Zagazig, Egypt.
School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, China.
PLoS One. 2020 Jun 26;15(6):e0235187. doi: 10.1371/journal.pone.0235187. eCollection 2020.
COVID-19 is a worldwide epidemic, as announced by the World Health Organization (WHO) in March 2020. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. In this paper, a new ML-method proposed to classify the chest x-ray images into two classes, COVID-19 patient or non-COVID-19 person. The features extracted from the chest x-ray images using new Fractional Multichannel Exponent Moments (FrMEMs). A parallel multi-core computational framework utilized to accelerate the computational process. Then, a modified Manta-Ray Foraging Optimization based on differential evolution used to select the most significant features. The proposed method evaluated using two COVID-19 x-ray datasets. The proposed method achieved accuracy rates of 96.09% and 98.09% for the first and second datasets, respectively.
2020 年 3 月，世界卫生组织宣布 COVID-19 疫情为全球性流行病。机器学习（ML）方法可以通过对胸部 X 光图像进行可视化分析，在识别 COVID-19 患者方面发挥重要作用。本文提出了一种新的 ML 方法，用于将胸部 X 光图像分为 COVID-19 患者或非 COVID-19 人群两类。使用新的分数多通道指数矩（FrMEMs）从胸部 X 光图像中提取特征。利用并行多核计算框架加速计算过程。然后，使用基于差分进化的改进的曼塔射线觅食优化算法选择最重要的特征。使用两个 COVID-19 X 射线数据集对所提出的方法进行评估。该方法在第一个数据集和第二个数据集上的准确率分别达到了 96.09%和 98.09%。
