# COVID-19 Diagnosis from Crowdsourced Cough Sound Data
## 4 Experiments
### Results  
Table 4 shows the sensitivity, specificity and accuracy of each row. In experiments (a) to (c), only MFCC was extracted from each database and analyzed with LSTM. Experiment (a) had a sensitivity and specificity of about 60% and 62%, respectively, and (b) had a sensitivity and specificity of about 77% and 65%, respectively. Experiment (c) had a sensitivity and specificity of about 71% and 76%, respectively, higher than (a) and (b). The lowest performance was found in (a), using relatively high-quality data. In addition, in (b) using the smallest number of data, the performance was relatively higher than that of (a) and (c). This fact suggests that more high-quality data was needed. Experiment (d) produced a diagnostic accuracy of about 88% to 90%, which was lower than (e) and (f). However, (d) showed that the ResNet-50 model was effective in diagnosing COVID-19 cough sounds. Experiment (e) was the model excluding the clinical feature in [13]. Here, the clinical features indicated whether the individual had fever symptoms or underlying respiratory illness. We extracted a new 36-dimensional feature set and spectrogram from [21] and trained the ResNet-50 + DNN model. It showed sensitivity and specificity of 93% and 94%, respectively, and had higher diagnostic accuracy than (e), even though the clinical features were not used.  
For the experiment shown in Table 3, each ROC curve is as shown in Figure 8. The proposed method had an AUC value of 0.98 at the maximum area under the ROC curve as the red line (f). The blue line (e) had an AUC value of 0.96, which was lower than that of the proposed method. The orange line depicts method (d), and has an AUC value of about 0.95, lower than the red line (f) and the blue line (e). Below that, they show lower performance than the above three experimental results. From these results, it was found that the extracted feature set including the spectrograms and MFCC were important to distinguish the cough sound of COVID-19 positive people from the cough sound of healthy people. In addition, when the ResNet-50+DNN model was trained with this feature set, it showed higher diagnostic accuracy than [13].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Database** & **Features** & **Model** & \multicolumn{3}{c}{**Performance**} \\ \cline{4-6}  & & & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline (a) & [21] & MFCC & LSTM & 62\% & 60\% & 62\% \\ (b) & [12] & MFCC & LSTM & 71\% & 77\% & 65\% \\ (c) & [20] & MFCC & LSTM & 73\% & 71\% & 76\% \\ (d) & [21] & Spectrogram & ResNet-50 & 88\% & 90\% & 88\% \\ (e) & [21] & MFCC + spectrogram & ResNet-50 + DNN & 89\% & 93\% & 86\% \\ (f) & [21] & Proposed feature set + spectrogram & ResNet-50 + DNN & 94\% & 93\% & 94\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison results of each study.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Specifications** \\ \hline Operation system & Ubuntu 18.04 LTS \\ Tensorflow & 2.4.1 \\ Cuda & 10.1 \\ CPU & intel Core i7-4770 \\ GPU & GeForce GTX 1080 Ti \(\times\) 1 \\ RAM & 16 GB \\ \hline \hline \end{tabular}
\end{table}
Table 3: Specifications of software and hardware used in the experiment.