# A Novel Fused Multi-Class Deep Learning Approach for Chronic Wounds Classification
## 2 Literature Review  
This section discusses the existing approaches for the classification of chronic wound types. The existing approaches for wound type classification are mainly focused on binary classification; only a few studies have performed the multi-class classification. For instance, [PERSON]. [16; 17] introduced the CNN-based approaches for classifying diabetic foot ulcers. [PERSON] et al. [18] suggested the customized ResNet50 for identifying healthy and burned skin, and achieved satisfactory results. In Ref. [19], an ensemble CNN network was introduced for the binary classification of infection and ischemia in diabetic wounds. [PERSON] et al. [20] applied the transfer learning approach and presented a model based on Mobilenet-v1 to classify macerations in diabetic foot ulcers. The model attained an accuracy of 69% on the dataset having the 416 wound images collected from the wound care center of Christliches Klinikum Melle, Germany. Similarly, in Ref. [21], the Xception model with pre-trained weights was employed to classify diabetic and venous ulcers. The model was evaluated on a private wound image dataset (909 wound images), and 83% and 67% accuracy were achieved for cropped and full wound images, respectively. Some studies [22; 23; 24; 25] have also performed multi-class chronic wound classification. For instance, [PERSON] et al. [22] introduced an ensemble classifier consisting of two AlexNet classifiers for multi-class wound image classification. One classifier extracted the image-wise information, while the other processed the patch-level information using the sliding window technique. Finally, the combined output from the two classifiers was passed to the Multilayer Perceptron (MLP) to predict the class label. Patch generation and processing in patch-wise classifiers increase the ensemble model complexity regarding time and computational cost. For 3-class classification (diabetic, pressure, andvenous-arterial), average accuracies of 82.9% and 87.7% were obtained on the Medetec and AZH datasets, respectively. This method [22] has acceptable accuracy for classifying venous and surgical wounds, but it shows the lowest accuracy while classifying pressure and diabetic wounds. Overall, the model has good accuracy, but at the expense of time complexity. [PERSON] et al. [23] suggested an approach that not only classified the wound images but also provided an explanation via the visualization of crucial areas where the model concentrated while classifying the wounds. The presented classifier employed pre-trained VGG-16, having to unfreeze the last three layers, along with the softmax layer. However, importance maps were generated by utilizing the LIME XAI tool and heatmap for explainability. The model was evaluated on their custom wound dataset, with the classes being named diabetic, pressure, surgical, and lymphovascular wound. This model [23] also showed lower results for diabetic and pressure wounds. In Ref. [24], a multi-modal network was presented that concatenated the two classifiers: (i) wound image classifier (WIC) and (ii) wound location classifier (WLC). The multi-modal classifier utilized the wound images and their corresponding locations for classifying the wound into one of the wound classes (diabetic, pressure, venous, or surgical). It is observed that utilizing only locations for wound classification yields a lower accuracy than when using only wound images, indicating that the data are not location-dependent. The CNN-based model demonstrated in [25] was evaluated on their private dataset with five wound types: deep wound, infected wound, arterial wound, venous wound, and pressure wound. The CNN network shared the same attention module-based backbone model, with task-specific branches. This model [25] has a classification accuracy of less than 75% for infected and deep wounds. In wound diagnosis, wound severity classification can also aid clinicians in categorizing wound conditions. The transfer learning approach and stacked deep learning models were utilized to classify wound severity [26].