Sadhana S, Pandiarajan S, Sivaraman E, Daniel D
Department of CSE, Kalaignarkarunanidhi Institute of Technology, Coimbatore, India.
Department of CSE, PES University, Bangalore, India.
Procedia Comput Sci. 2021;194:255-271. doi: 10.1016/j.procs.2021.10.081. Epub 2021 Dec 3.
Globally, the confirmed coronavirus (SARS-CoV2) cases are being increasing day by day. Coronavirus (COVID-19) causes an acute infection in the respiratory tract that started spreading in late 2019. Huge datasets of SARS-CoV2 patients can be incorporated and analyzed by machine learning strategies for understanding the pattern of pathological spread and helps to analyze the accuracy and speed of novel therapeutic methodologies, also detect the susceptible people depends on their physiological and genetic aspects. To identify the possible cases faster and rapidly, we propose the Artificial Intelligence (AI) power screening solution for SARS- CoV2 infection that can be deployable through the mobile application. It collects the details of the travel history, symptoms, common signs, gender, age and diagnosis of the cough sound. To examine the sharpness of pathomorphological variations in respiratory tracts induced by SARS-CoV2, that compared to other respiratory illnesses to address this issue. To overcome the shortage of SARS-CoV2 datasets, we apply the transfer learning technique. Multipronged mediator for risk-averse Artificial Intelligence Architecture is induced for minimizing the false diagnosis of risk-stemming from the problem of complex dimensionality. This proposed application provides early detection and prior screening for SARS-CoV2 cases. Huge data points can be processed through AI framework that can examine the users and classify them into "Probably COVID", "Probably not COVID" and "Result indeterminate".
在全球范围内,确诊的冠状病毒(SARS-CoV2)病例正在逐日增加。冠状病毒(COVID-19)会引发呼吸道急性感染,该感染于2019年末开始传播。通过机器学习策略可以整合和分析大量SARS-CoV2患者数据集,以了解病理传播模式,有助于分析新型治疗方法的准确性和速度,还能根据生理和遗传因素检测易感人群。为了更快地识别可能的病例,我们提出了针对SARS-CoV2感染的人工智能(AI)强力筛查解决方案,该方案可通过移动应用程序进行部署。它收集旅行史、症状、常见体征、性别、年龄以及咳嗽声音诊断等细节。为了研究SARS-CoV2引起的呼吸道病理形态学变化的清晰度,将其与其他呼吸道疾病进行比较以解决此问题。为了克服SARS-CoV2数据集的不足,我们应用迁移学习技术。引入用于规避风险的人工智能架构的多管齐下的调解器,以尽量减少因复杂维度问题导致的风险误判。此提议的应用程序可为SARS-CoV2病例提供早期检测和预先筛查。可以通过人工智能框架处理大量数据点,该框架可以检查用户并将他们分类为“可能感染新冠”、“可能未感染新冠”和“结果不确定”。