# Automatic COVID-19 Detection from Cough Sounds Using Multi-Headed Convolutional Neural Networks
## 4 Experimental Dataset1
### Experimental Results and Analysis  
To compare the performances of MHCNN and single-headed CNN, the MHCNN is divided into three independent single-headed CNNs, which are named Head1 CNN, Head2 CNN, and Head3 CNN. In order to compare the effects of MHCNN and three single-headed CNN (Head1 CNN, Head2 CNN, and Head3 CNN), we, respectively, used three different features (Mfcc + mel, Mfcc + mel + VGG embeddings, and VGG embeddings). The accuracy is shown in Table 1, where Mfcc + mel + VGG embeddings feature, and MHCNN model (accuracy with 98.09%) outperforms another methods. Moreover, different feature performances (Mfcc + mel, Mfcc + mel + VGG embeddings, and VGG embeddings) with the same model and different model performances (Head1 CNN, Head2 CNN, Head3 CNN, and MHCNN) with the same feature are also evaluated by AUC value. The results are shown in Table 2, where it can be seen that the combination of Mfcc + mel + VGG embeddings feature and MHCNN model has the highest AUC value (AUC = 0.9959). In this study, The model.fit() function is used to train the model for a certain number of epochs, and return historical training data. The learning curves for accuracy and loss function are shown in Figures 4 and 5. The model was trained 38 times, and by the time of the 30 th epoch, the network had gradually converged. It can be seen from the two figures that the generalization performance of the network is satisfied, and the two curves on the training set and the validation set are relatively close. Other evaluation indicators, including precision, recall rate, and F1 score are also calculated and shown in Table 3. Among 1360 test samples, there are 680 negative data and 680 positive data. The precision on the negative data is 0.99, the recall rate is 0.97, and the F1 score is 0.98. The precision on positive data is 0.97, the recall rate is 0.99, and the F1 score is 0.98. The results of three evaluation metrics show that MHCNN model performs well in the test set. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Accuracy** & **Head1 CNN** & **Head2 CNN** & **Head3 CNN** & **MHCNN** \\ \hline Mfcc + mel & 96.62\% & 97.28\% & 97.79\% & 97.13\% \\ Mfcc + mel + VGG embeddings & 97.28\% & 94.93\% & 96.91\% & **98.09\%** \\ VGG embeddings & 91.03\% & 93.75\% & 95.15\% & 95.29\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracy of the combination of four different models and three different features on the AICovidVN 115M challenge dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**AUC** & **Head1 CNN** & **Head2 CNN** & **Head3 CNN** & **MHCNN** \\ \hline Mfcc + mel & 0.9909 & 0.9948 & 0.9942 & 0.9929 \\ Mfcc + mel + VGG embeddings & 0.9938 & 0.9908 & 0.9948 & **0.9959** \\ VGG embeddings & 0.9782 & 0.9926 & 0.9835 & 0.9858 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: AUC of the combination of four different models and three different features on the AICovidVN 115M challenge dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**AUC** & **Head1 CNN** & **Head2 CNN** & **Head3 CNN** & **MHCNN** \\ \hline Mfcc + mel & 0.9909 & 0.9948 & 0.9942 & 0.9929 \\ Mfcc + mel + VGG embeddings & 0.9938 & 0.9908 & 0.9948 & **0.9959** \\ VGG embeddings & 0.9782 & 0.9926 & 0.9835 & 0.9858 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: AUC of the combination of four different models and three different features on the AICovidVN 115M challenge dataset. At the same time, the confusion matrix is adapted to evaluate and supervise the performance of the learning algorithm, and reflect the parts that the real value is consistent with the predicted value or not. The confusion matrix of the MHCNN model is shown below in Figure 6. From the perspective of lines, the first line represents the actual number of cough audio of healthy people in the test audio, and the second line represents the actual number of cough audio of patients with COVID-19.