## 5 Simulation Result and Discussion
### Training Results  
Figure 14 shows the accuracy and loss of training behaviors during the training process. Figure 14a shows the accuracy curves with the increments in batch-size. All networks were trained using the learning rate of \(10^{-3}\). Among all, ResNet and XceptionNet tuned out to have the highest accuracy rates, while DenseNet had the lowest one. DenseNet was found to be the slowest CNN that reached convergence when trained on the thermal DFU images. Two mobile-based networks, MobileNetV2 and ShuffleNet, had similar accuracy values to the beginning of the training process.  
In Figure 14c, the accuracy rates of ShuffleNet and DenseNet increased since the training started. DenseNet reached convergence more slowly than ShuffleNet and other networks. On the other hand, decreasing the learning rate decreased the starting accuracy of EfficientNet, MobileNetV2, and XceptionNet. Overall, DenseNet and and EfficientNet showed slower convergence achievements. The graphs show that the higher the learning rate value. the faster the training accuracy towards convergence. Fluctuated training curves also inform that the dataset images has low-variance as a result of the augmentation process.  
Figure 14b,d,f show the training loss values for six CNNs trained on the DFU dataset. Based on Figure 14b, the models trained using the learning rate value of \(10^{-3}\) showed high fluctuation trends. It possibly means that the variance within the dataset is low because the dataset contains augmented data. XceptionNet showed unstable performance and hit a lower curve when the training epoch was between 25 and 50. However, the curve rose at from epochs 50 to 75 and dropped again at epochs above 75. The curve of ResNet seemed to follow that of the XceptionNet and experienced a similar shape change. The curves of EfficientNet and DenseNet looked more dominant at the top of the training loss curve. The MobileNetV2 and ShuffleNet curves are in the middle of the loss curve. Figure 14d shows the training loss curve at the learning rate of 0.0005 or \(5\times 10^{-4}\). The XceptionNet and ResNet curves hit the lowest points, just like the curves in Figure 14b.  
The curve shown in Figure 14f is a curve of the training models at the learning rate of \(10^{-4}\). The EfficientNet model had the highest loss value during the training process, followed by that of DenseNet, MobileNetV2, ShuffleNet, ResNet, and XceptionNet. According to Figure 14b,d,f, the learning rates affected the movements of each model's weight towards convergence. The smaller the learning rates, the less the training loss values fluctuated. Oppositely, each curve in Figure 14a,c,e exhibited a more stable trend.  
Figure 14: Training accuracy during the training process. (**a,b**): Trained using learning rate of \(10^{-3}\). (**c,d**): Trained using learning rate of \(5\times 10^{-4}\). (**e,f**): Trained using learning rate of \(10^{-4}\).  
Figure 15 shows the learning curve of the CNN models. Based on the learning curve, all CNN models showed that the training accuracy increased to a stability point. The validation accuracy also showed similar trends. Every model in the simulation showed that the training and the validation accuracy has a small gap. Furthermore, the testing accuracy is under the training and validation accuracy. This trend indicated that the models have a good fit to the learning curve.