DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image

被引：67
作者：
Hasan N. [1 ]
Bao Y. [1 ]
Shawon A. [2 ]
Huang Y. [3 ]
机构：
[1] Center for Modern Information Management, School of Management, Huazhong University of Science and Technology, Wuhan
[2] Frontier Semiconductor Bangladesh Ltd., Dhaka
[3] Center for Big Data Analytics, Jiangxi University of Engineering, Xinyu
来源：
SN Computer Science | 2021年 / 2卷 / 5期
关键词：
COVID-19; CT image; Deep learning; DenseNet-121;
D O I：
10.1007/s42979-021-00782-7
中图分类号：
学科分类号：
摘要：
Recently, the destructive impact of Coronavirus 2019, commonly known as COVID-19, has affected public health and human lives. This catastrophic effect disrupted human experience by introducing an exponentially more damaging unpredictable health crisis since the Second World War (Kursumovic et al. in Anaesthesia 75: 989–992, 2020). Strong communicable characteristics of COVID-19 within human communities make the world's crisis a severe pandemic. Due to the unavailable vaccine of COVID-19 to control rather than cure, early and accurate detection of the virus can be a promising technique for tracking and preventing the infection from spreading (e.g., by isolating the patients). This situation indicates improving the auxiliary COVID-19 detection technique. Computed tomography (CT) imaging is a widely used technique for pneumonia because of its expected availability. The artificial intelligence-aided images analysis might be a promising alternative for identifying COVID-19. This paper presents a promising technique of predicting COVID-19 patients from the CT image using convolutional neural networks (CNN). The novel approach is based on the most recent modified CNN architecture (DenseNet-121) to predict COVID-19. The results outperformed 92% accuracy, with a 95% recall showing acceptable performance for the prediction of COVID-19. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
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