DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image.
DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image.
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DOI：
10.1007/s42979-021-00782-7
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Huang Y
中科院分区：
文献类型：
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作者：
Hasan N;Bao Y;Shawon A;Huang Y
最近，冠状病毒2019年，俗称新冠肺炎的破坏性影响，已经影响到公共卫生和人类生命。这种灾难性的影响扰乱了人类的体验，自第二次世界大战以来引入了破坏性更大的不可预测的健康危机(KurSumovic等人。《麻醉学》75：989-992,2020)。新冠肺炎在人类社区内的强烈传染性特征使世界危机成为一种严重的流行病。由于无法获得新冠肺炎的疫苗来控制而不是治愈，及早和准确地检测病毒可能是一种很有前途的跟踪和防止感染传播的技术(例如，通过隔离患者)。这种情况表明辅助新冠肺炎检测技术正在改进。计算机断层扫描(CT)成像是肺炎的一种广泛使用的技术，因为它的预期可用性。人工智能辅助的图像分析可能是识别新冠肺炎的一个很有前途的替代方案。本文提出了一种利用卷积神经网络从CT图像预测新冠肺炎患者的方法。这种新的方法是基于最新修改的美国有线电视新闻网架构(DenseNet-121)来预测新冠肺炎。结果的准确率超过了92%，95%的召回率表明对于新冠肺炎的预测表现出可以接受的性能。
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.