Mishra Shreyas
National Institute of Technology, Rourkela, India.
SN Comput Sci. 2021;2(5):390. doi: 10.1007/s42979-021-00785-4. Epub 2021 Jul 24.
The Coronavirus Disease 2019 (COVID-19) which first emerged in Wuhan, China in late December, 2019, has now spread to all the countries in the world. Conventional testing methods such as the antigen test, serology tests, and polymerase chain reaction tests are widely used. However, the test results can take anything from a few hours to a few days to reach the patient. Chest CT scan images have been used as alternatives for the detection of COVID-19 infection. Use of CT scan images alone might have limited capabilities, which calls attention to incorporating clinical features. In this paper, deep learning algorithms have been utilized to integrate the chest CT scan images obtained from patients with their clinical characteristics for fast and accurate diagnosis of COVID-19 patients. The framework uses an ANN to obtain the probability of the patient being infected with COVID-19 using their clinical information. Beyond a certain threshold, the chest CT scan of the patient is classified using a deep learning model which has been trained to classify the CT scan with 99% accuracy.
2019年12月下旬首次在中国武汉出现的2019冠状病毒病（COVID-19），现已蔓延至世界各国。抗原检测、血清学检测和聚合酶链反应检测等传统检测方法被广泛使用。然而，检测结果可能需要几个小时到几天才能送达患者手中。胸部CT扫描图像已被用作检测COVID-19感染的替代方法。仅使用CT扫描图像的能力可能有限，这就需要关注纳入临床特征。在本文中，深度学习算法已被用于将从患者获得的胸部CT扫描图像与其临床特征相结合，以快速、准确地诊断COVID-19患者。该框架使用人工神经网络（ANN），根据患者的临床信息获得其感染COVID-19的概率。超过一定阈值后，使用经过训练的深度学习模型对患者的胸部CT扫描进行分类，该模型对CT扫描的分类准确率为99%。
