## 3 Results
### Classification  
[PERSON] et al. [13] introduced DFU_QUTNet, a deep convolutional neural network (CNN), for classifying DFUs into healthy or abnormal skin categories. The researchers collected the dataset from a hospital in Iraq, capturing both healthy (normal) and DFU-affected skin (abnormal). The researchers augmented the images using rotation, flipping, and contrast adjustments to address the dataset's small size. Unlike traditional convolutional neural networks, where increasing the depth by stacking more layers can degrade performance due to issues like those related to gradient, DFU_QUTNet is designed with a wider network while maintaining depth, ensuring better gradient propagation. The DFU_QUTNet architecture incorporates 17 convolutional layers, batch normalization, Rectified Linear Unit (ReLU) activations, and global average pooling, resulting in robust feature extraction, such as edges. DFU_QUTNet outperformed GoogleNet, VGG16, AlexNet, and DFUNet [29]. Additionally, SVM outperformed k-nearest neighbor (KNN) when combined with DFU_QUTNet. [PERSON] et al. [30] introduced a deep learning framework for the classification and localization of DFUs, focusing on two complications such as infection (bacteria in the wound) and ischemia (inadequate blood supply to the affected area). The framework employs a 16-layer CNN, which includes three convolutional layers, three batch layers, three ReLU layers, one average pooling layer, one skip convolutional layer, one addition  
Figure 2: The purposes of the reviewed studies. layer, one fully connected layer, one softmax layer, and one classification output layer, enabling effective feature extraction and classification. For localization, the CNN is paired with the YOLOv2-DFU model, built on YOLOv2 with ShuffleNet as a backbone. The researchers evaluated the model's performance using the DFU-Part (B) dataset [31]. The data were augmented to enhance model robustness. Classification experiments employed multiple machine learning classifiers, with the Decision Tree (DT) and Naive Bayes (NB) achieving the highest accuracy for infection and ischemia. After classification, gradient-weighted class activation mapping was utilized to validate the model's decisions. [PERSON] et al. [15] addressed the classification of DFU ischemia and infection using the DFU2020 dataset [32]. The dataset was expanded through augmentation techniques like rotation, flipping, scaling, translation, noise addition, and shearing. Transfer learning with fine-tuning was employed to overcome the challenges of limited medical image datasets, enabling effective DFU classification. Several CNN architectures were evaluated, including AlexNet, VGG16/19, GoogLeNet, ResNet50/101, MobileNet, SqueezeNet, and DenseNet. ResNet50 achieved the best performance due to its skip connections, which allowed for deeper network training and mitigated vanishing gradient issues. This approach highlights the effectiveness of combining transfer learning with a robust CNN architecture for addressing challenges in small, specialized medical datasets. [PERSON] et al. [33] introduced DFU-SIAM, a deep learning model, for classifying DFUs using a Siamese Neural Network (SNN) architecture that integrates EfficientNetV2S (CNN) and BEiT (Vision Image Transformer) for feature extraction. Researchers trained the DFU-SIAM model on the DFUC2021 dataset [34], containing four classes for training: none, infection, ischemia, and both. This severe class imbalance is addressed through data augmentation techniques, including color jitter, random horizontal and vertical flips, and equalization, creating a more balanced training dataset. The Siamese architecture comprises two identical networks, extracting features from pairs of input images for similarity learning. EfficientNetV2S extracts detailed convolutional features, while BEiT captures global image contexts through masked image modeling. The model flattens, concatenates, and passes the outputs from both subnetworks to a classification layer.