Taresh Mundher Mohammed, Zhu Ningbo, Ali Talal Ahmed Ali, Alghaili Mohammed, Hameed Asaad Shakir, Mutar Modhi Lafta
College of Information Science and Engineering, Hunan University, Changsha, Hunan, China.
Department of Mathematics, General Directorate of Thi-Qar Education, Ministry of Education, Thi-Qar, Iraq.
PeerJ Comput Sci. 2021 Sep 20;7:e694. doi: 10.7717/peerj-cs.694. eCollection 2021.
The emergence of the novel coronavirus pneumonia (COVID-19) pandemic at the end of 2019 led to worldwide chaos. However, the world breathed a sigh of relief when a few countries announced the development of a vaccine and gradually began to distribute it. Nevertheless, the emergence of another wave of this pandemic returned us to the starting point. At present, early detection of infected people is the paramount concern of both specialists and health researchers. This paper proposes a method to detect infected patients through chest x-ray images by using the large dataset available online for COVID-19 (COVIDx), which consists of 2128 X-ray images of COVID-19 cases, 8,066 normal cases, and 5,575 cases of pneumonia. A hybrid algorithm is applied to improve image quality before undertaking neural network training. This algorithm combines two different noise-reduction filters in the image, followed by a contrast enhancement algorithm. To detect COVID-19, we propose a novel convolution neural network (CNN) architecture called KL-MOB (COVID-19 detection network based on the MobileNet structure). The performance of KL-MOB is boosted by adding the Kullback-Leibler (KL) divergence loss function when trained from scratch. The KL divergence loss function is adopted for content-based image retrieval and fine-grained classification to improve the quality of image representation. The results are impressive: the overall benchmark accuracy, sensitivity, specificity, and precision are 98.7%, 98.32%, 98.82% and 98.37%, respectively. These promising results should help other researchers develop innovative methods to aid specialists. The tremendous potential of the method proposed herein can also be used to detect COVID-19 quickly and safely in patients throughout the world.
2019年末新型冠状病毒肺炎（COVID-19）大流行的出现导致了全球范围的混乱。然而，当一些国家宣布研发出疫苗并逐渐开始分发时，全世界都松了一口气。尽管如此，这一波大流行的再次出现又让我们回到了起点。目前，早期检测出感染者是专家和健康研究人员最为关注的事情。本文提出了一种利用在线可得的用于COVID-19的大型数据集（COVIDx）通过胸部X光图像检测感染患者的方法，该数据集由2128张COVID-19病例的X光图像、8066张正常病例图像和5575张肺炎病例图像组成。在进行神经网络训练之前，应用一种混合算法来提高图像质量。该算法在图像中结合了两种不同的降噪滤波器，随后是一种对比度增强算法。为了检测COVID-19，我们提出了一种名为KL-MOB（基于MobileNet结构的COVID-19检测网络）的新型卷积神经网络（CNN）架构。在从头开始训练时，通过添加库尔贝克-莱布勒（KL）散度损失函数来提升KL-MOB的性能。KL散度损失函数被用于基于内容的图像检索和细粒度分类，以提高图像表示的质量。结果令人印象深刻：总体基准准确率、灵敏度、特异性和精确率分别为98.7%、98.32%、98.82%和98.37%。这些令人鼓舞的结果应有助于其他研究人员开发创新方法来协助专家。本文所提出方法的巨大潜力还可用于在全球范围内快速、安全地检测患者是否感染COVID-19。
