Habib Mohammed, Ramzan Muhammad, Khan Sajid Ali
Department of Computer Science, College of Computing and Informatics, Saudi Electronic University, 11673 Riyadh, Saudi Arabia.
Department of Electrical Engineering, Faculty of Engineering, PortSaid University, Port Said, 42526 Egypt.
J Grid Comput. 2022;20(3):23. doi: 10.1007/s10723-022-09615-0. Epub 2022 Jul 18.
The world has witnessed dramatic changes because of the advent of COVID19 in the last few days of 2019. During the last more than two years, COVID-19 has badly affected the world in diverse ways. It has not only affected human health and mortality rate but also the economic condition on a global scale. There is an urgent need today to cope with this pandemic and its diverse effects. Medical imaging has revolutionized the treatment of various diseases during the last four decades. Automated detection and classification systems have proven to be of great assistance to the doctors and scientific community for the treatment of various diseases. In this paper, a novel framework for an efficient COVID-19 classification system is proposed which uses the hybrid feature extraction approach. After preprocessing image data, two types of features i.e., deep learning and handcrafted, are extracted. For Deep learning features, two pre-trained models namely ResNet101 and DenseNet201 are used. Handcrafted features are extracted using Weber Local Descriptor (WLD). The Excitation component of WLD is utilized and features are reduced using DCT. Features are extracted from both models, handcrafted features are fused, and significant features are selected using entropy. Experiments have proven the effectiveness of the proposed model. A comprehensive set of experiments have been performed and results are compared with the existing well-known methods. The proposed technique has performed better in terms of accuracy and time.
由于2019年末新冠病毒病的出现，世界发生了巨大变化。在过去两年多的时间里，新冠病毒病以多种方式对世界产生了严重影响。它不仅影响了人类健康和死亡率，还对全球经济状况产生了影响。如今迫切需要应对这一疫情及其各种影响。在过去的四十年里，医学成像彻底改变了各种疾病的治疗方式。自动检测和分类系统已被证明对医生和科学界治疗各种疾病有很大帮助。本文提出了一种用于高效新冠病毒病分类系统的新颖框架，该框架采用混合特征提取方法。在对图像数据进行预处理后，提取两种类型的特征，即深度学习特征和手工制作的特征。对于深度学习特征，使用了两个预训练模型，即ResNet101和DenseNet201。使用韦伯局部描述符（WLD）提取手工制作的特征。利用WLD的激励分量，并使用离散余弦变换（DCT）减少特征。从两个模型中提取特征，融合手工制作的特征，并使用熵选择重要特征。实验证明了所提出模型的有效性。进行了一系列全面的实验，并将结果与现有的知名方法进行了比较。所提出的技术在准确性和时间方面表现更好。
