## 4 Results and Discussions
### Performance Analysis of the Multi-Label Disease Classification Model  
The DenseNet-121 model is utilised for the classification of diseases. The dataset comprised seven distinct disease labels and an additional one, which consisted of all diseases with fewer than 10 subjects in each category. Model training stopped at 41 epochs. The disease classification model with DenseNet-121 architecture took over 18 h to train. Table 4 shows the performance parameters for the eight labels considered in the test data of 474 images. The ROC curve in Figure 6 gives an idea of true positive predictions for each category.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Disease** & **Precision** & **F1-Score** & **Accuracy** \\ \hline DM & 0.9722 & 0.8203 & 0.9148 \\ \hline BP & 0.9803 & 0.8658 & 0.9425 \\ \hline APD & 0.9130 & 0.8038 & 0.9240 \\ \hline Pyrexia & 0.9473 & 0.9183 & 0.9703 \\ \hline Hepatitis & 0.9885 & 0.8958 & 0.9629 \\ \hline Cold Cough & 0.9878 & 0.8901 & 0.9629 \\ \hline Gastritis & 0.9798 & 0.9652 & 0.9870 \\ \hline Others & 0.9034 & 0.7553 & 0.8093 \\ \hline \end{tabular}
\end{table}
Table 4: Performance Parameters for the Disease Classification Model.  
Figure 5: Comparison bar graph for 5-folds and ensemble techniques.  
Highlighted rows in the table indicate inaccurate classification for two sample images. The results for the class \"others\" were also observed to be slightly less accurate. This is also evident from the ROC curve and performance indices.  
The proposed model for multi-disease classification shows satisfactory results. The most important outcome of this work is that images captured in different environments and on various mobile phones demonstrated appreciable results. Further improvement is possible with an ensemble of different deep learning models to achieve high accuracy for all disease labels. As it is not practically possible for any individual model to deliver uniform accuracy for all classification labels under consideration, multiple models will ultimately enhance the overall prediction accuracy. In future work, the aim is to increase the number of disease labels to encompass all potential cases that tongue analysis can diagnose, in collaboration with a medical expert to compile ground truth data for classification.  
Even with the limitation of a small dataset with a smaller variety of disease classes, model performance is appreciable. Since only a single dense model is considered, its performance accuracy for all class labels could not be uniform. As previously mentioned, we can enhance this by incorporating the ensemble learning method.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Test Image Samples**} & \multicolumn{8}{c}{**Prediction Probability**} & \multirow{2}{*}{**Truth**} \\ \cline{2-2} \cline{5-8}  & **DM** & **BP** & **APD** & **PYR** & **HEP** & **CC** & **GAS** & **Others** \\ \hline  & 0.0059 & 0.0235 & **0.7697** & 0.0071 & 0.0074 & 0.0315 & **0.9693** & **0.8268** & APD, gastritis, others \\ \hline  & 0.0004 & 0.0103 & **0.9436** & 0.0413 & 0.0029 & 0.0142 & **0.9705** & **0.7960** & APD, gastritis, others \\ \hline  & 0.0335 & 0.0188 & 0.0108 & 0.0987 & **0.4677** & 0.0124 & **0.7906** & 0.0795 & Hepatitis, gastritis \\ \hline  & 0.1365 & 0.0628 & 0.2372 & 0.0261 & 0.0131 & 0.2798 & 0.0423 & **0.5847** & Pyrexia \\ \hline  & 0.0264 & 0.0036 & 0.1321 & 0.1261 & 0.0083 & **0.4880** & 0.1867 & 0.3476 & Cold cough \\ \hline  & 0.9768 & **0.8526** & 0.0079 & 0.0013 & 0.0009 & **0.9293** & 0.0010 & **0.9519** & DM, hypertension, cold cough, others \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._