Nneji Grace Ugochi, Deng Jianhua, Monday Happy Nkanta, Hossin Md Altab, Obiora Sandra, Nahar Saifun, Cai Jingye
School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Healthcare (Basel). 2022 Feb 21;10(2):403. doi: 10.3390/healthcare10020403.
Computed Tomography has become a vital screening method for the detection of coronavirus 2019 (COVID-19). With the high mortality rate and overload for domain experts, radiologists, and clinicians, there is a need for the application of a computerized diagnostic technique. To this effect, we have taken into consideration improving the performance of COVID-19 identification by tackling the issue of low quality and resolution of computed tomography images by introducing our method. We have reported about a technique named the modified enhanced super resolution generative adversarial network for a better high resolution of computed tomography images. Furthermore, in contrast to the fashion of increasing network depth and complexity to beef up imaging performance, we incorporated a Siamese capsule network that extracts distinct features for COVID-19 identification.The qualitative and quantitative results establish that the proposed model is effective, accurate, and robust for COVID-19 screening. We demonstrate the proposed model for COVID-19 identification on a publicly available dataset COVID-CT, which contains 349 COVID-19 and 463 non-COVID-19 computed tomography images. The proposed method achieves an accuracy of 97.92%, sensitivity of 98.85%, specificity of 97.21%, AUC of 98.03%, precision of 98.44%, and F1 score of 97.52%. Our approach obtained state-of-the-art performance, according to experimental results, which is helpful for COVID-19 screening. This new conceptual framework is proposed to play an influential task in the issue facing COVID-19 and related ailments, with the availability of few datasets.
计算机断层扫描已成为检测2019冠状病毒病（COVID-19）的重要筛查方法。鉴于其高死亡率以及领域专家、放射科医生和临床医生面临的工作负担过重问题，需要应用计算机化诊断技术。为此，我们考虑通过引入我们的方法来解决计算机断层扫描图像质量低和分辨率低的问题，从而提高COVID-19识别的性能。我们报告了一种名为改进增强超分辨率生成对抗网络的技术，以实现计算机断层扫描图像更好的高分辨率。此外，与通过增加网络深度和复杂性来增强成像性能的方式不同，我们纳入了一个暹罗胶囊网络，用于提取COVID-19识别的独特特征。定性和定量结果表明，所提出的模型对于COVID-19筛查是有效、准确且稳健的。我们在一个公开可用的COVID-CT数据集上展示了所提出的用于COVID-19识别的模型，该数据集包含349张COVID-19计算机断层扫描图像和463张非COVID-19计算机断层扫描图像。所提出的方法实现了97.92%的准确率、98.85%的灵敏度、97.21%的特异性、98.03%的AUC、98.44%的精确率和97.52%的F1分数。根据实验结果，我们的方法取得了领先的性能，这有助于COVID-19筛查。在可用数据集较少的情况下，这个新的概念框架被提出来在应对COVID-19及相关疾病的问题中发挥重要作用。
