## 3 Results
### Segmentation
[21] introduced Double Encoder-ResUNet (DE-ResUNet), a deep learning model designed to segment diabetic foot thermal images by combining RGB and thermal data to improve segmentation accuracy. They utilized a dataset of 398 paired RGB and thermal images from the National Hospital Dos de Mayo in Peru. The researchers applied augmentation techniques like horizontal flipping, rotations, and contrast changes to address the limited sample size. The DE-ResUNet model combines the strengths of residual network and UNet architecture, adopting an encoder-decoder structure. Unlike a traditional UNet, DE-ResUNet uses two parallel encoders to process thermal and RGB images separately. DE-ResUNet outperformed all competitors, such as UNet and SegNet. [PERSON] et al. [50] proposed a system that integrates transfer learning and Fast R-CNN for object detection to enhance the diagnosis of diabetic foot wounds (DFWs). They employed transfer learning to harness pre-trained models, such as ResNet101 and Inception V2, for robust feature extraction and effective classification of wound types. Three Fast R-CNN modules, iNaturalist Species-trained (ResNet101), Kitti-trained (ResNet101), and Inception V2-coco, were used to evaluate performance. Among these, the Kitti-trained ResNet101 module demonstrated superior accuracy and performance. After detecting the wounds, they employed classical image processing methods like the GrabCut algorithm and SURF technique for segmentation. The dataset, provided by Taichung Veterans General Hospital, includes 727 images categorized into ulcers, sutures, and blood vessel blockages. To address data limitations, augmentation techniques such as flipping, rotation, and distortion were applied, expanding the dataset to 900 images per category for a total of 3600 images. The system also features a web-based interface, allowing medical professionals to upload and analyze images and view results. [PERSON] et al. [20] presented an approach for the automated segmentation of foot ulcers, addressing the extraction of morphological features from foot wounds. The authors proposed an ensemble of two encoder-decoder CNN architectures, UNet and LinkNet, integrated with pre-trained EfficientNet backbones, EfficientNetB1 for LinkNet and EfficientNetB2 for UNet, to enhance segmentation accuracy. To address limited data availability, the models were pre-trained on the Medetec dataset [51]. To optimize segmentation, the authors implemented five-fold cross-validation for generalizability, test time augmentation for prediction robustness, and result fusion through averaging predictions. The researchers tested the method on the Chronic Wound Dataset [52] and the extended FUSeg dataset [53]. This approach was ranked first in the FUSeg challenge. [PERSON] et al. [54] introduced FusionSegNet, a deep learning framework designed to improve the diagnosis of DFUs by integrating global foot features, e.g., skin wrinkles and abnormalities, with local wound features, e.g., wound depth, area, and location. The model operates in two stages: first, it utilizes a segmentation module to isolate wound areas using UNet pre-trained on the MICCAI FUSC2021 dataset [55]. In the second stage, a classification network based on ResNet-34 is employed to extract global and local features. The method is evaluated on the dataset collected by Shanghai Municipal Eighth People's Hospital. Compared to existing methods, such as Inception-ResNet-v2, ResNet without CBAM, and DFUNet, FusionSegNet demonstrated the ability to distinguish DFUs from other chronic wounds. [PERSON] et al. [56] introduced FUSegNet, an advanced deep learning model specifically developed to segment DFUs. FUSegNet is built on an encoder-decoder architecture. The encoder leverages EfficientNet-b7 to feature extraction. The decoder incorporates a parallel spatial and channel Squeeze-and-Excitation module, which combines additive and max-out operations. A modified version, x-FUSegNet, utilizes five-fold cross-validation to improve segmentation in complex backgrounds.