Automatic detection and prediction of COVID-19 in cough audio signals using coronavirus herd immunity optimizer algorithm

Abstract

The global spread of COVID-19, particularly through cough symptoms, necessitates efficient diagnostic tools. COVID-19 patients exhibit unique cough sound patterns distinguishable from other respiratory conditions. This study proposes an advanced framework to detect and predict COVID-19 using deep learning from cough audio signals. Audio data from the COUGHVID dataset undergo preprocessing through fuzzy gray level difference histogram equalization, followed by segmentation with a U-Net model. Key features are extracted via Zernike Moments (ZM) and Gray Level Co-occurrence Matrix (GLCM). The Enhanced Deep Neural Network (EDNN), tuned by the Coronavirus Herd Immunity Optimizer (CHIO), performs final prediction by minimizing error metrics. Comparative simulation results reveal that the proposed EDNN-CHIO model improves MSE by 25.35% and SMAPE by 42.06% over conventional models like PSO, WOA, and LSTM. The proposed approach demonstrates superior error reduction, highlighting its potential for effective COVID-19 detection.

Keywords: COVID-19 detection and prediction; Coronavirus herd immunity optimizer; Cough audio signals; Enhanced deep neural network.

Fig. 1

Graphical abstract.

Fig. 2

Proposed COVID-19 prediction model.

Fig. 3

EDNN-based COVID-19 cough prediction model.

Algorithm 1

CHIO pseudo code

Fig. 4

MSE analysis.

Fig. 5

SMAPE analysis.

Fig. 6

MAE analysis.

Fig. 7

RMSE analysis.

Fig. 8

MAPE analysis.

Fig. 9

Classification accuracy analysis.

Fig. 10

Confusion matrix analysis.

Fig. 11

Precision analysis.

Fig. 12

Recall analysis.

Fig. 13

F1 score analysis.

Fig. 14

AUC score analysis.

Fig. 15

Training and validation loss analysis.

Fig. 16

Training and validation accuracy analysis.