COVID-19 Detection with a Novel Multi-Type Deep Fusion Method using Breathing and Coughing Information

This study explores the use of deep learning-based methods for the automatic detection of COVID-19. Specifically, we aim to investigate the involvement of the virus in the respiratory system by analysing breathing and coughing sounds. Our hypothesis resides in the complementarity of both data types for the task at hand. Therefore, we focus on the analysis of fusion mechanisms to enrich the information available for the diagnosis. In this work, we introduce a novel injection fusion mechanism that considers the embedded representations learned from one data type to extract the embedded representations of the other data type. Our experiments are performed on a crowdsourced database with breathing and coughing sounds recorded using both a web-based application, and a smartphone app. The results obtained support the feasibility of the injection fusion mechanism presented, as the models trained with this mechanism outperform single-type models and multi-type models using conventional fusion mechanisms.