## 5 Simulation Result and Discussion
### Decision Fusion Results  
In Figure 17, it was shown that ResNet, Efficient-Net, ShuffleNet, and MobileNetV2 all had a sensitivity of 1.0 at the learning rate of \(10^{-3}\). Therefore, we then fused ResNet, Efficient-Net, ShuffleNet, and MobileNetV2 with DenseNet and ShuffleNet, which had specificities of 1.0, to classify the DFU images. The results are shown in Table 2.  
Table 2 shows the decision fusion result of several models. Most of the models, including the proposed ones, achieved 100% accuracy rate. The proposed method is distinguished from other fusion models because the proposed model required far less learning parameters and has a smaller size. For example, the proposed model only has 3.08 M learning parameters and a 11.37 MB model size, while the fusion of MobileNetv2 and DenseNet reached 20.31 M learning parameters and a 74.45 MB model size (both are 6.5x larger than the proposed model).  
According to the confusion matrix in Figure 18, it was confirmed that the proposed method demonstrated the best performance. When the model was only trained using a single model, the accuracy was only 93.34%. Nevertheless, when the model was fused, the accuracy increased to 100%. Based on the proposed method, the drawback of MobileNetv2 in classifying the CONTROL images was solved by ShuffleNet and vice-versa.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Deep Learning Based**} \\ \hline \multirow{2}{*}{**Fusion Methods**} & **Learning** & \multirow{2}{*}{**Accuracy**} & **Sensitivity** & \multirow{2}{*}{**Specificity**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**F-Measure**} & **Model** \\  & **Parameters** & & & & & & **Size** \\ \hline ResNet + DenseNet & 60.64 M & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 217.3 MB \\ Xception + DenseNet & 38.94 M & 0.967 & 0.934 & 1.000 & 1.000 & 0.967 & 140.4 MB \\ EfficientNet + DenseNet & 22.11 M & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 80.8 MB \\ ShuffleNet + DenseNet & 18.94 M & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 69.52 MB \\ MobileNetv2 + DenseNet & 20.31 M & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 74.45 MB \\ ResNet + ShuffleNet & 43.41 M & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 154.22 MB \\ DenseNet + ShuffleNet & 18.94 M & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 69.52 MB \\ Xception + ShuffleNet & 21.71 M & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 77.32 MB \\ EfficientNet + ShuffleNet & 4.88 M & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 17.72 MB \\
**MobileNetv2 + ShuffleNet** & **3.08 M** & **1.000** & **1.000** & **1.000** & **1.000** & **1.000** & **11.37 MB** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Decision fusion performance for thermal DFU classification. Bold font indicates considerable results.  
Figure 17: The sensitivity values of six CNN benchmarking architectures.  
Fusing MobileNetV2 and ShuffleNet resulted in a promising performance. This result is confirmed by visualizing the activation map generated by MobileNetV2 and ShuffleNet. Activation mapping layers were used to identify and illustrate a CNN model at a specific layer. Figure 19 shows the activation map of the last activation layer from MobileNetV2 and ShuffleNet. The upper row indicates positive images while the lower row indicates negative images.  
According to Figure 19, positive and negative images generated by the last activation layer of MobileNetV2 and ShuffleNet had different mapping intensities. The intensity values of the positive patients' images were dominant in the contralateral region. The images display an asymmetrical thermal distribution. On the other hand, thermal distributions of the negative patients' images are symmetrical. Thus, it can be concluded that MobileNetV2 and ShuffleNet can represent the thermal distributions of diabetic feet more effectively using the activation layer. Based on Figure 19, ShuffleNet provides a more focused representation than MobileNetV2 to indicate a positive or negative patient.