# A Novel Fused Multi-Class Deep Learning Approach for Chronic Wounds Classification
## 4 Experimental Setup and Results
### Performance Evaluation of the Proposed Model
#### 4.4.4 Evaluation on the Extended AZH Dataset  
We experimented on the Extended AZH dataset to check the model performance for the balanced dataset. For this purpose, we performed a 3-class classification experiment. The experimental protocols are the same as for the 3-class classification experiment in Section 4.4.3, and the details of the training and testing samples in each class are provided in Table 1. The model achieved 88.17% accuracy and 88.34% recall, precision, and F1-score. The confusion matrix is illustrated in Figure 9.  
The classification accuracy for the Diabetic and Pressure wound classes improved, as observed from the confusion matrix. However, compared to the results from the AZH dataset, a decrease in the accuracy of the Venous wound class was noticed. We observed the _FP_s of 10 and _FN_s of 4 for Venous wounds. The Diabetic wound class has 8 _FN_ instances that are misclassified as Pressure and Venous wounds. This is because Diabetic wounds are identical to Pressure wounds in terms of shape, while they are similar in texture to Venous wounds. Generally, the overall classification accuracy of the model has improved because of the balanced dataset. Figure 8: Confusion Matrix for AZH 6-class. Some of the wound images that are misclassified by our model are presented in Figure 10. Overall, it is observed that the shapes and textures of some diabetic wounds are similar to the pressure wounds on the foot, such as having a fluid texture and some yellow slough around the wound. Also, some of the diabetic wounds have a texture that is similar to the venous wounds; for instance, a dry crust. Therefore, diabetic wounds are misclassified as pressure and venous wounds, and vice versa. Pressure wounds having scalp and dry crust are misclassified as venous wounds, due to the similarity in structure to venous wounds. Likewise, the venous wound with liquid and yellow slough on it is wrongly classified as a pressure wound. Figure 10: Misclassified Samples. Figure 9: Confusion Matrix for Extended AZH. The summarized performance of our proposed SEEN-B4 model, in terms of accuracy, balanced accuracy, precision, recall and F1-score, on the Medetec, Extended Medetec, AZH, and Extended AZH datasets, is presented in Table 2. In Figure 11, we also presented the area under the receiver operating characteristic (ROC) curves. In the case of both the 3-class and 6-class classifications, Figure 11 shows that we have a high area under the curve (greater than 92%) for each wound class, except for the Diabetic wound class of the Medetec dataset. Since the Diabetic class has very few samples compared to other two classes in the Medetec dataset; therefore, when we increased the diabetic samples in the Extended Medetec dataset, the area under the curve for Diabetic class became 95%. The high area under the ROC curve indicates the ability of our model to effectively discriminate and classify the multiple types of wounds. The AZH and Medetec datasets have a class imbalance, while the class imbalance is diminished in the Extended AZH and Extended Medetec datasets. These datasets contain images that can have inconsistent standards because of the image acquisition being performed by multiple personnel, having distinct imaging equipment, differences in the angle and distance of the capture, and variations in the imaging content such as light intensity, color mode, etc. Consequently, this makes the classification task a challenging one. However, the experimental results demonstrate that our proposed framework can distinguish the different types of chronic wounds, such as Diabetic, Pressure, Venous, and Surgical wounds.