A Systematic Review of Multimodal Deep Learning Approaches for COVID-19 Diagnosis

Salvatore Capuozzo, Carlo Sansone

Abstract: During and after the years of the COVID-19 pandemic, researchers and domain experts put all their effort into the discovery of accurate and reliable techniques for the detection and diagnosis of this disease in potentially sick patients. In the meanwhile, Deep Learning (DL) techniques are continuously improving and expanding, becoming more and more efficient and compatible in several fields of study and with different kinds of data. This huge but heterogeneous set of data cannot be fully exploited if DL models are not designed to be compatible with different sources of data at the same time, therefore multimodal approaches were designed and adopted, resulting in better prediction results than the classic approaches. Given these premises, several multimodal solutions for COVID-19 diagnosis were built in these years, but it may result hard to have a complete overview of the current state-of-the-art. For this reason, this paper wants to be a useful review of multimodal approaches and related adopted datasets, and therefore a starting point to quickly check what to improve to bring more accurate solutions.

External IDs:doi:10.1007/978-3-031-51026-7_13