LSTM-Based COVID-19 Detection Method Using Coughing

COVID-19 has disrupted and irrevocably changed the everyday lives of people all around the world. This viral disease has created the necessity for a contact-free, non-invasive, and easy-to-use diagnostic device. In this paper, we propose a smartphone-based COVID-19 detection method that detects COVID-19 based on the coughing sound of patients. The proposed algorithm segments the coughing sounds collected from the raw audio signals acquired by a smartphone and then detects COVID-19 from the segmented coughing sounds. The proposed algorithm puts raw coughing sounds and the features extracted from the raw sounds into long-term short memory (LSTM), which is known to be effective in the regression and classification of periodic time series signals. Experimental results show that the proposed method applied to the Virufy dataset provides COVID-19 detection accuracy of 92% from the coughing segments. The proposed method has an advantage in pre-diagnosing COVID-19 since the proposed method only requires a smartphone Index Terms—COVID-19, LSTM., machine learning.