Mubarak Auwalu Saleh, Serte Sertan, Al-Turjman Fadi, Ameen Zubaida Sa'id, Ozsoz Mehmet
Department of Electrical and Electronics Engineering Near East University Mersin Turkey.
Department of Artificial Intelligence, Research Center for AI and IoT Near East University Mersin Turkey.
Expert Syst. 2022 Mar;39(3):e12842. doi: 10.1111/exsy.12842. Epub 2021 Sep 29.
The deadly coronavirus virus (COVID-19) was confirmed as a pandemic by the World Health Organization (WHO) in December 2019. It is important to identify suspected patients as early as possible in order to control the spread of the virus, improve the efficacy of medical treatment, and, as a result, lower the mortality rate. The adopted method of detecting COVID-19 is the reverse-transcription polymerase chain reaction (RT-PCR), the process is affected by a scarcity of RT-PCR kits as well as its complexities. Medical imaging using machine learning and deep learning has proved to be one of the most efficient methods of detecting respiratory diseases, but to train machine learning features needs to be extracted manually, and in deep learning, efficiency is affected by deep learning architecture and low data. In this study, handcrafted local binary pattern (LBP) and automatic seven deep learning models extracted features were used to train support vector machines (SVM) and K-nearest neighbour (KNN) classifiers, to improve the performance of the classifier, a concatenated LBP and deep learning feature was proposed to train the KNN and SVM, based on the performance criteria, the models VGG-19 + LBP achieved the highest accuracy of 99.4%. The SVM and KNN classifiers trained on the hybrid feature outperform the state of the art model. This shows that the proposed feature can improve the performance of the classifiers in detecting COVID-19.
致命的冠状病毒（COVID-19）于2019年12月被世界卫生组织（WHO）确认为大流行病。尽早识别疑似患者对于控制病毒传播、提高医疗效果并进而降低死亡率至关重要。检测COVID-19所采用的方法是逆转录聚合酶链反应（RT-PCR），该过程受到RT-PCR试剂盒短缺及其复杂性的影响。利用机器学习和深度学习的医学成像已被证明是检测呼吸道疾病最有效的方法之一，但在机器学习中需要手动提取特征，而在深度学习中，效率受到深度学习架构和低数据量的影响。在本研究中，手工制作的局部二值模式（LBP）和自动提取特征的七个深度学习模型被用于训练支持向量机（SVM）和K近邻（KNN）分类器，为提高分类器性能，提出了一种将LBP和深度学习特征拼接的方法来训练KNN和SVM，基于性能标准，VGG-19 + LBP模型达到了99.4%的最高准确率。在混合特征上训练的SVM和KNN分类器优于现有模型。这表明所提出的特征可以提高分类器在检测COVID-19方面的性能。
