Coughing-based recognition of covid-19 with spatial attentive ConvLSTM recurrent neural networks

The rapid emergence of COVID-19 has become a major public health threat around the world. Although early detection is crucial to reduce its spread, the existing diagnostic methods are still insufficient in bringing the pandemic under control. Thus, more sophisticated systems, able to easily identify the infection from a larger variety of symptoms, such as cough, are urgently needed. Deep learning models can indeed convey numerous signal features relevant to fight against the disease; yet, the performance of state-of-the-art approaches is still severely restricted by the feature information loss typically due to the high number of layers. To mitigate this phenomenon, identifying the most relevant feature areas by drawing into attention mechanisms becomes essential. In this paper, we introduce Spatial Attentive ConvLSTM-RNN (SACRNN), a novel algorithm that is using Convolutional Long-Short Term Memory Recurrent Neural Networks with embedded attention that has the ability to identify the most valuable features. The promising results achieved by the fusion between the proposed model and a conventional Attentive Convolutional Recurrent Neural Network, on the automatic recognition of COVID-19 coughing (73.2 % of Unweighted Average Recall) show the great potential of the presented approach in developing efficient solutions to defeat the pandemic.