## 5 Simulation Result and Discussion
### Discussion  
The simulations demonstrated that a single CNN model struggles to cope with fewer training images, and this issue can be solved by using the fusion strategy. Fewer training images produce a dataset consisting of low-variance images, which results in highly fluctuating learning curves. The curves also indicate that the trained models might be \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Deep Learning Based**} \\ \hline
**Methods** & **Accuracy** & **Recall** & **Precision** & **F-Measure** &
\begin{tabular}{c} **Learning** \\ **Parameters** \\ \end{tabular} \\ \hline The proposed model & **1.000** & **1.000** & **1.000** & **1.000** & **3.08 M** \\ ESNet & **1.000** & **1.000** & **1.000** & **1.000** & 4.88 M \\ Cruz-Vega [7] & 0.820 & 0.550 & **1.000** & 0.860 & 0.12 M \\ Khan [48] & 0.770 & 0.920 & 0.790 & 0.850 & 4.90 M \\ Resnet101 & 0.90 & 0.934 & 0.875 & 0.904 & 44.6 M \\ MS+MP & 0.770 & 0.920 & 0.790 & 0.850 & 3.08 M \\ Khandakar [14] & 0.9581 & 0.9581 & 0.9584 & 0.9582 & 2.30 M \\ \hline \hline \multicolumn{6}{c}{**Conventional Machine Learning Based**} \\ \hline LBP+SVM & 0.880 & 0.917 & 0.917 & 0.917 & - \\ LBP+NB & 0.710 & 0.890 & 0.670 & 0.760 & - \\ LBP+RF & 0.880 & 0.917 & 0.917 & 0.917 & - \\ AdaBoost + Random Forest [14] & 0.9671 & 0.9795 & 0.9755 & 0.9775 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison performance of the proposed DF with other methods. Bold fonts indicate considerable results. under-fitting models. Fusing the decisions of two models makes the classification task easier, proven by the maximum accuracy value that it achieves. The proposed decision fusion model was developed based on the results of the sensitivity and specificity of each model. To recognize the negative patient image, ShuffleNet is activated due to it ability to recognize positive patients well, and vice versa for MobileNetv2. Therefore, the accuracy can reach a score of 1.00. Based on the activation mapping in Figure 19, it was confirmed that fusing MobileNetv2 and ShuffleNet resulted in advantages for DFU classification because both models represent positive and negative images. The proposed fusion strategy resulted in a model with the fewest learning parameters, yet with excellent performances across all evaluation metrics. Combining EfficientNet and MobileNetV2 with ShuffleNet resulted in 4.88 M and 3.08 M learning parameters, respectively, which is less than those of the EfficientNetB0 (ImageNet model) or NasNet-mobile, designed specifically for mobile devices, with 5.3 M learning parameters. Therefore, the proposed fusion rule can be used for CNN model development on mobile devices. The proposed method has an accuracy of 1.0, which means that all DFU patients were classified correctly. In medical applications, it is very important to classify patients correctly, so that the patient's issue can be detected earlier and handled properly by medical experts. The proposed model, which was a decision-rule-based approach, resulted in a high accuracy rate. These results strengthen the contribution of this study for early DFU detection using thermal imaging. Decision fusion can also contribute to early detection cases in other medical imaging fields. Classification using a single model has not been able to produce a generalized model that performs well on thermal DFU images. This was caused by several factors, including limited and unbalanced datasets. In dealing with those challenges, the study used a data augmentation approach, but data augmentation has great potential to be class-dependent [50]. Another challenge is the dataset availability. There is only one public dataset which was used in this study, whereas training a CNN requires a large and heterogeneous dataset to produce a well-generalized model. To improve the DFU classification performance, it was suggested to increase the number of datasets in the training stage. It would very helpful to increase model performances especially on single CNN models.