## 3 Results
### Classification
A KNN classifier is incorporated for final predictions, with the optimal number of neighbors determined dynamically based on the maximized Macro F1 score during training. DFU-SIAM leverages deep learning and network design to the DFU classification across multiple categories. [PERSON] et al. [14] presented a system for classifying infection and ischemia in DFUs using the EfficientNet deep learning architecture. It utilizes the publicly available DFUC2021 dataset [34], and unlike previous studies that attempted multi-class classification, this work simplifies the task into two binary classifications: infection (yes/no) and ischemia (yes/no). EfficientNet models from B0 to B7 variants were fine-tuned using transfer learning, where the early layers pre-trained on ImageNet were retained to extract generic features, and later layers were retrained to focus on specific wound features. The researchers used baseline models, including ResNet50, DenseNet121, Inception V3, and VGG16, for comparison. EfficientNet demonstrated superior performance and was significantly faster, completing tasks in 10-50% of the time required by baseline models, making them practical for real-time use. [PERSON] et al. [35] presented a deep learning-based model for classifying diabetic foot sores (DFSs), specifically targeting abrasion foot sores (AFSs) and ischemic diabetic foot sores. They utilized two publicly available datasets, one from the Kaggle repository [36] and another from [PERSON] et al. [13]. To overcome data scarcity, data augmentation techniques such as flipping, zooming, and noise reduction were used to expand the dataset to 8478 images. The authors proposed an architecture combining a pre-trained VGG-19 model for feature extraction and a custom six-layer CNN for classification. Imagesegmentation was performed using UNet++, a densely connected U-shaped architecture that enhances feature map resolution and minimizes segmentation errors, which is critical for accurately delineating lesion boundaries. Comparisons were made with models such as Inception-v3 and MobileNet, with the proposed model outperforming these baselines in all metrics. Furthermore, statistical analysis using ANOVA and Friedman tests validated the model's robustness. [PERSON] et al. [37] proposed a deep learning-based system to classify DFUs as normal or abnormal using the Adaptive Weighted Sub-Gradient Convolutional Neural Network (AWSgCNN). The dataset was sourced from Kaggle [36]. Researchers preprocessed the data by removing missing or inconsistent entries and splitting the dataset into 80% training and 20% testing subsets. The AWSg-CNN integrates two key components: the random initialization of weights (RIW) and the Adaptive Sub-gradient Optimizer (ASGO). RIW helps prevent overfitting and ensures effective learning by adapting to high-dimensional data, while the ASGO stabilizes gradients and optimizes convergence rates through a log softmax function. The model achieved high classification accuracy, precision, recall, and F1 scores. [PERSON] et al. [38] proposed a deep learning framework for analyzing foot ulcer images in diabetic patients, with a focus on classification and feature extraction. It uses the DFUC2021 dataset [34]. They integrated a Deep Recurrent Neural Network (DRNN) for feature extraction from numerical and text data with a Pre-trained Fast Convolutional Neural Network (PFCNN) integrated with U++Net for image classification. The model processes preprocessed and segmented data to distinguish between normal and abnormal diabetes ranges. [PERSON] et al. [39] proposed real-time classification models for distinguishing normal and abnormal DFUs using deep learning techniques combined with hardware acceleration platforms. The study utilized a dataset sourced from the Kaggle repository [36], where data augmentation was applied to address class imbalance. Two CNN models, DFU_FNet and DFU_TFNet, were introduced, each designed to optimize feature extraction and mitigate the limitations of small datasets.