Manocha Ankush, Bhatia Munish
Lovely Professional University, Phagwara, 144411, Punjab, India.
Comput Electr Eng. 2022 Oct;103:108274. doi: 10.1016/j.compeleceng.2022.108274. Epub 2022 Aug 3.
Over the last two years, the novel coronavirus has become a significant threat to the health of the public, and numerous approaches are developed to determine the symptoms of COVID-19. To deal with the complex symptoms of COVID-19, a Deep Learning-assisted Multi-modal Data Analysis (DMDA) approach is introduced to determine COVID-19 symptoms by utilizing acoustic and image-based data. Furthermore, the classified events are forwarded to the proposed Dynamic Fusion Strategy (DFS) for confirming the health status of the individual. Initially, the performance of the proposed solution is evaluated on both acoustic and image-based samples and the proposed solution attains the maximum accuracy of 96.88% and 98.76%, respectively. Similarly, the DFS has achieved an overall symptom determination accuracy of 98.72% which is highly acceptable for decision-making. Moreover, the proposed solution shows high reliability with an accuracy of 95.64% even in absence of any one of the data modalities during testing.
在过去两年中，新型冠状病毒已成为对公众健康的重大威胁，人们开发了多种方法来确定新冠肺炎的症状。为了应对新冠肺炎的复杂症状，引入了一种深度学习辅助多模态数据分析（DMDA）方法，通过利用声学和基于图像的数据来确定新冠肺炎症状。此外，分类后的事件被转发到所提出的动态融合策略（DFS），以确认个体的健康状况。最初，在所提出的解决方案在基于声学和基于图像的样本上进行评估，该解决方案分别达到了96.88%和98.76%的最高准确率。同样，DFS实现了98.72%的总体症状确定准确率，这对于决策来说是高度可接受的。此外，所提出的解决方案即使在测试期间缺少任何一种数据模态的情况下，也显示出95.64%的准确率，具有很高的可靠性。
