A novel deep fusion strategy for COVID-19 prediction using multimodality approach. (October 2022) - Manocha, Ankush; Bhatia, Munish
Abstract: 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. Graphical abstract: Highlights: To develop a COVID-19 monitoring framework that utilizes different data modalities. To develop deep learning-based semi-supervised solution for symptom determination. To fuse different predicted outcomes, a dynamic fusion strategy is proposed. To justify the performance by comparing the outcomes with traditional solutions.