Cough-Based COVID-19 Detection with Contextual Attention Convolutional Neural Networks and Gender Information

Abstract

The aim of this contribution is to automatically detect COVID-19 patients by analysing the acoustic information embedded in coughs. COVID-19 affects the respiratory system, and, consequently, respiratory-related signals have the potential to contain salient information for the task at hand. We focus on analysing the spectrogram representations of cough samples with the aim to investigate whether COVID-19 alters the frequency content of these signals. Furthermore, this work also assesses the impact of gender in the automatic detection of COVID-19. To extract deep-learnt representations of the spectrograms, we compare the performance of a cough-specific, and a Resnet18 pre-trained Convolutional Neural Network (CNN). Additionally, our approach explores the use of contextual attention, so the model can learn to highlight the most relevant deep-learnt features extracted by the CNN. We conduct our experiments on the dataset released for the Cough Sound Track of the DICOVA 2021 Challenge. The best performance on the test set is obtained using the Resnet18 pre-trained CNN with contextual attention, which scored an Area Under the Curve (AUC) of 70.91 % at 80 % sensitivity.

Towards sound based testing of COVID-19—Summary of the first Diagnostics of COVID-19 using Acoustics (DiCOVA) Challenge

“…The challenge turnaround time was days, and the progress made by different teams in this short time span highlighted their efforts. Eleven studies pursued in this challenge ( Muguli et al, 2021 , Das et al, 2021 , Mallol-Ragolta et al, 2021 , Ritwik et al, 2021 , Deshpande and Schuller, 2021 , Karas and Schuller, 2021 , Bhosale et al, 2021 , Södergren et al, 2021 , Harvill et al, 2021 , Kamble et al, 2021 , Avila et al, 2021 ), after going through the peer review process, were presented at the DiCOVA Special Session, Interspeech 2021 Conference (on Aug 2021).…”

MSCCov19Net: multi-branch deep learning model for COVID-19 detection from cough sounds

“…The need for computerized analysis for fast and accurate diagnosis comes to the fore during this pandemic. Several works using automatic deep learning algorithms on CT scans [6][7][8][9][10] and machine learning algorithms on cough sounds [11][12][13][14][15][16][17][18][19][20][21][22] are proposed in literature. The works on CT scans [6][7][8][9][10] provide information about the degree of severity of the individual's lung damage.…”

Reliability of crowdsourced data and patient-reported outcome measures in cough-based COVID-19 screening

“…Once the predictive features are selected, we feed these into classifiers to predict the subjects' COVID status. To ensure our results are generalizable to a broad variety of machine learning approaches, we tested a portfolio of seven binary classifiers that belong to three families: two statistical classifiers (Logistic Regression (LR) 49 and Linear Discriminative Analysis (LDA) 50,51 ), two ensemble-based classifiers (Random Forest (RF) 52 and Gradient Boosting Classifier (XGB) 53 ), and three deep learning classifiers (Deep Neural Network (DNN) 54 , CNN-RNN 55 and Contextual Attention CNN (CA-CNN) 56 ). LR and LDA exploit statistical inference to produce the probability of an instance being a member of each class.…”