基于6层深度卷积神经网络的COVID-19检测

IF 2.2 4区工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY

Cmes-computer Modeling in Engineering & Sciences Pub Date : 2022-01-01 DOI:10.32604/cmes.2022.016621

Shouming Hou, Ji Han

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引用次数: 3

摘要

世界各地有许多人因COVID-19而丧生。大多数新冠肺炎患者的症状是发烧、疲倦和干咳，疾病很容易传播给周围的人。如果能够及早发现感染者，这将有助于地方当局控制病毒的传播速度，感染者也可以及时得到治疗。为了提高COVID-19患者的检测效果，我们提出了一种结合max池化、批处理归一化和Adam算法的六层卷积神经网络。在10倍交叉验证方法中，我们的方法优于几种最先进的方法。此外，我们利用Grad-CAM技术实现热图可视化，观察模型训练和检测的过程。

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COVID-19 Detection via a 6-Layer Deep Convolutional Neural Network

Many people around the world have lost their lives due to COVID-19. The symptoms of most COVID-19 patients are fever, tiredness and dry cough, and the disease can easily spread to those around them. If the infected people can be detected early, this will help local authorities control the speed of the virus, and the infected can also be treated in time. We proposed a six-layer convolutional neural network combined with max pooling, batch normalization and Adam algorithm to improve the detection effect of COVID-19 patients. In the 10-fold cross-validation methods, our method is superior to several state-of-the-art methods. In addition, we use Grad-CAM technology to realize heat map visualization to observe the process of model training and detection.

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来源期刊

Cmes-computer Modeling in Engineering & Sciences ENGINEERING, MULTIDISCIPLINARY-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

CiteScore

3.80

自引率

16.70%

发文量

298

审稿时长

7.8 months

期刊介绍： This journal publishes original research papers of reasonable permanent value, in the areas of computational mechanics, computational physics, computational chemistry, and computational biology, pertinent to solids, fluids, gases, biomaterials, and other continua. Various length scales (quantum, nano, micro, meso, and macro), and various time scales ( picoseconds to hours) are of interest. Papers which deal with multi-physics problems, as well as those which deal with the interfaces of mechanics, chemistry, and biology, are particularly encouraged. New computational approaches, and more efficient algorithms, which eventually make near-real-time computations possible, are welcome. Original papers dealing with new methods such as meshless methods, and mesh-reduction methods are sought.