COVID-19 Intelligent Detection System Based on Cloud-Edge Fusion Recognition Model

Abstract

Analyzing cough sounds can help with the quick detection of COVID-19. A cloud-edge deep learning fusion-based intelligent detection system for COVID-19 is proposed in this paper. In the cloud-side, a COVID-19 detection model based on ResNet18 is employed, with log-Mel-spectrum characteristics used as inputs. In the edge-side, a COVID-19 detection model based on TCNN is developed using raw audio inputs. To improve the detection accuracy, result fusion is carried out in the cloud-side after getting the recognition results from both models. On the test dataset, the fusion model attained a sensitivity of 0.8012, an AUC of 0.8251, and a specificity of 0.7255. According to comparative testing results, the fusion model outperforms the other models in classification performance and is less prone to false-positive errors. It provides a novel way to COVID-19 recognition and performs well as an auxiliary detection method.