M V Manoj Kumar, Atalla Shadi, Almuraqab Nasser, Moonesar Immanuel Azaad
Department of Information Science and Engineering, Nitte Meenakshi Institute of Technology, Bangalore, India.
College of Engineering & Information Technology, University of Dubai, Dubai, United Arab Emirates.
Front Artif Intell. 2022 May 27;5:912022. doi: 10.3389/frai.2022.912022. eCollection 2022.
Graphical-design-based symptomatic techniques in pandemics perform a quintessential purpose in screening hit causes that comparatively render better outcomes amongst the principal radioscopy mechanisms in recognizing and diagnosing COVID-19 cases. The deep learning paradigm has been applied vastly to investigate radiographic images such as Chest X-Rays (CXR) and CT scan images. These radiographic images are rich in information such as patterns and clusters like structures, which are evident in conformance and detection of COVID-19 like pandemics. This paper aims to comprehensively study and analyze detection methodology based on Deep learning techniques for COVID-19 diagnosis. Deep learning technology is a good, practical, and affordable modality that can be deemed a reliable technique for adequately diagnosing the COVID-19 virus. Furthermore, the research determines the potential to enhance image character through artificial intelligence and distinguishes the most inexpensive and most trustworthy imaging method to anticipate dreadful viruses. This paper further discusses the cost-effectiveness of the surveyed methods for detecting COVID-19, in contrast with the other methods. Several finance-related aspects of COVID-19 detection effectiveness of different methods used for COVID-19 detection have been discussed. Overall, this study presents an overview of COVID-19 detection using deep learning methods and their cost-effectiveness and financial implications from the perspective of insurance claim settlement.
基于图形设计的大流行症状技术在筛查致病原因方面发挥着至关重要的作用，相比主要的放射检查机制，在识别和诊断新冠肺炎病例方面能产生更好的结果。深度学习范式已被广泛应用于研究诸如胸部X光（CXR）和CT扫描图像等放射图像。这些放射图像包含丰富的信息，如结构模式和聚类等，在新冠肺炎类大流行疾病的一致性和检测中很明显。本文旨在全面研究和分析基于深度学习技术的新冠肺炎诊断检测方法。深度学习技术是一种良好、实用且经济实惠的方式，可被视为充分诊断新冠病毒的可靠技术。此外，该研究确定了通过人工智能增强图像特征的潜力，并区分出预测可怕病毒的最廉价且最可靠的成像方法。本文还将所调查的新冠肺炎检测方法与其他方法进行对比，讨论了其成本效益。探讨了用于新冠肺炎检测的不同方法在检测有效性方面的几个与财务相关的方面。总体而言，本研究从保险理赔结算的角度概述了使用深度学习方法进行新冠肺炎检测及其成本效益和财务影响。
