# A Novel Fused Multi-Class Deep Learning Approach for Chronic Wounds Classification
## 4 Experimental Setup and Results
### Comparative Study  
We compared the proposed SEEN-B4 framework with the existing contemporary wound classification methods [22; 24] to assess its effectiveness over the comparative approaches for wound classification. Specifically, the performance of SEEN-B4 on the AZH dataset is compared with the wound image classifier (WIC) [24] and wound multimodality classifier (WMC) [24]. Furthermore, for the Medetec dataset, we compared the results with the ensemble classifier [22], WIC [24], and WMC [24]. In WMC, the outputs of two models (image and location classifiers) were concatenated to produce the final prediction, while WIC only considered the image of the wound for classification. A VGG-based backbone network was employed in both classifiers. VGG consists of stacked convolutional layers with relatively more parameters than EfficientNet, which may affect its performance. In contrast, EfficientNet efficiently and uniformly scales the model's width, depth, and resolution using compound scaling, allowing it to achieve a more excellent performance with fewer parameters than the VGG. A comparison of the methods for 6-class classification on the AZH dataset, and for 3-class classification on the AZH and Medetec datasets, is shown in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Datasets** & **Models** & **Accuracy (\%)** \\ \hline \multirow{4}{*}{AZH, 3-class} & WIC [24] & 76.06 \\  & WMC [24] & 84.51 \\  & SEEN-B4 (Ours) & 87.32 \\ \hline \multirow{4}{*}{AZH, 6-class} & WIC [24] & 75.64 \\  & WMC [24] & 82.48 \\  & SEEN-B4 (Ours) & 83.19 \\ \hline \multirow{4}{*}{Medetec, 3-class} & Ensemble Classifier [22] & 82.90 \\  & WIC [24] & 82.22 \\ \cline{1-1}  & WMC [24] & 86.67 \\ \cline{1-1}  & SEEN-B4 (Ours) & 88.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with Existing Methods on AZH and Medetec Datasets.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**EfficientNets**} & \multicolumn{3}{c}{**Medetec Dataset**} & \multicolumn{3}{c}{**AZH Dataset**} & \\ \cline{2-7}  & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline EfficientNet-B0 & 81.12 & 74 & 73 & 80.28 & 80 & 77 & 78.47 \\ EfficientNet-B1 & 84.45 & 79 & 82 & 75.35 & 76 & 74 & 74.99 \\ EfficientNet-B2 & 77.76 & 75 & 70 & 75.35 & 76 & 72 & 73.94 \\ EfficientNet-B3 & 85.00 & 83 & 78 & 71.12 & 70 & 70 & 70.00 \\ EfficientNet-B4 & 86.67 & 82 & 80 & 81.00 & 80 & 82 & 80.99 \\ EfficientNet-B5 & 84.45 & 85 & 76 & 76.00 & 75 & 77 & 77.00 \\ EfficientNet-B6 & 82.23 & 77 & 73 & 76.76 & 77 & 74 & 75.47 \\ EfficientNet-B7 & 82.23 & 82 & 72 & 76.76 & 78 & 74 & 75.96 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of EfficientNets on AZH Dataset.  
It is noticeable that the proposed SEEN-B4 model provided superior classification accuracy over the existing method, for both the 3-class and 6-class classifications. More precisely, our SEEN-B4 attained the highest accuracy on the AZH and Medetec datasets, while the WMC [24] is the second-best performing model. Additionally, for 6-class classification, our model obtained an average accuracy gain of 4% from the existing method. At the same time, an average accuracy gain of 7% and 4% was achieved for 3-class classification on the AZH and Medetec datasets, respectively. Overall, the proposed framework outperforms the contemporary wound classification methods for the multi-class classification on both datasets, demonstrating its capability to classify multiple wound types.