# Automatic COVID-19 Detection from Cough Sounds Using Multi-Headed Convolutional Neural Networks
## 5 Experimental Dataset2
### Experimental Results and Analysis  
To compare the performances of MHCNN and single-headed CNN, 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 4 and in Figure 8, where Mfcc + mel + VGG embeddings feature, and the MHCNN model (accuracy with 91.36%) outperforms another methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Accuracy** & **Head1 CNN** & **Head2 CNN** & **Head3 CNN** & **MHCNN** \\ \hline Mfcc + mel & 86.59\% & 86.59\% & 89.63\% & 88.41\% \\ Mfcc + mel + VGG embeddings & 82.72\% & 81.48\% & 87.04\% & **91.36**\% \\ VGG embeddings & 84.57\% & 79.01\% & 86.42\% & 89.51\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy of the combination of four different models and three different features on the University of Cambridge dataset. Figure 8: Accuracy bar charts of the combination of four different models and three different features on the University of Cambridge dataset. 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 5 and in Figure 9, where it can be seen that the combination of Mfcc + mel + VGG embeddings feature and MHCNN model has the highest AUC value (AUC = **0.9646**). 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 10 and 11. After training the neural network's 10 epochs, the network gradually converges. It can be seen the two curves on the training set and the validation set are relatively different, where the effect on training set is better than the validation set because the size of the dataset from the University of Cambridge is smaller than the AICovidVN 115M dataset. Other evaluation indicators, including precision, recall rate, and F1 score are, also calculated and shown in Table 6. Among 162 test samples, there are 81 negative data and 81 positive data. The precision on the negative data is 0.95, the recall rate is 0.88, and the F1 score is 0.91. The precision on positive data is 0.89, the recall rate is 0.95, and the F1 score is 0.92. The results of three evaluation metrics show that MHCNN model performs well in the test set. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Precision** & **Recall** & **F1** \\ \hline Negative & 0.95 & 0.88 & 0.91 \\ Positive & 0.89 & 0.95 & 0.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation indicators of the MHCNN model on the University of Cambridge dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**AUC** & **Head1 CNN** & **Head2 CNN** & **Head3 CNN** & **MHCNN** \\ \hline Mfcc + mel & 0.9289 & 0.9378 & 0.9491 & 0.9488 \\ Mfcc + mel + VGG embeddings & 0.8820 & 0.9053 & 0.9491 & **0.9646** \\ VGG embeddings & 0.9514 & 0.9114 & 0.9265 & 0.94734 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: AUC of the combination of four different models and three different features on the University of Cambridge dataset. Figure 9: AUC bar charts of the combination of four different models and three different features on the University of Cambridge 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.