## 1 Introduction
### Related Studies
Data augmentation techniques were applied to balance the less represented classes, such as rotation, Gaussian noise and contrast adjustments, which improved the model's generalisation capacity, especially for the ischaemia class. To interpret the predictions, the authors used Gradient-Weighted Class Activation Mapping (Grad-CAM) to visualise the areas focused on by the model during prediction, showing that in some cases it focused incorrectly outside the ulcer regions, leading to erroneous accuracies. Using the same dataset, in [PERSON] et al. [23], the EfficientNet model is explored, with the system achieving an accuracy of 99% for ischemia and 98% for infection. It is also reported that faster classification times can be achieved. Explainability methods are not mentioned, and performance indicators based on skin tone are absent. To categorize various wound types (diabetic, pressure, surgical and venous ulcers), a multimodal classifier is proposed by [PERSON] et al. [24]. Using the Medetec and the AZH datasets, the researchers designed three subsets with the help of wound specialists, each containing both image and location information. The multi-modal network itself was developed by concatenating the outputs of image-based and location-based classifiers, along with other modifications. The model achieved high accuracy, ranging from 82.48% to 100% in mixed-class classifications. [PERSON] et al. (2023) [25] developed a system for classifying DFUs into normal or abnormal based on DL, using the Adaptive Weighted Sub-Gradient Convolutional Neural Network (AWSg CNN). The dataset used was obtained from the Kaggle platform and was subjected to pre-processing, which included removing missing or inconsistent entries and dividing them into 80 per cent for training and 20 per cent for testing. Two key components are used for classification: random initialisation of weights (RIW), which helps prevent overfitting and ensures effective learning by adapting to high-dimensional data; and the Adaptive Sub-gradient Optimiser (ASGO), which stabilises gradients and optimises convergence rates using a log softmax function. The model achieved an accuracy, F1-score and area under the curve (AUC) of 99%. Also, with the aim of classifying DFU images into normal and abnormal, [PERSON] et al. (2023) [26] proposed a different deep learning framework. The DFUC2021 dataset was used, which was pre-processed and segmented. In addition, a deep recurrent neural network (DRNN) was integrated for feature extraction from numeric and text data with a fast convolutional neural network (PFCNN) pre-trained with U++ Net for image classification. The model obtained an accuracy of 99.32%. [PERSON] et al. (2024) [27] proposed real-time classification models to distinguish normal and abnormal DFUs, combining deep learning with hardware acceleration platforms. They used a dataset from the Kaggle repository and applied data augmentation to balance the classes. This study introduced two CNN models, DFU_FNet and DFU_TFNet, each designed to optimise feature extraction and mitigate the limitations of small datasets. DFU_FNet uses a simpler architecture to extract features for training classifiers such as SVM and KNN, while DFU_TFNet uses a deeper architecture reinforced with transfer learning, improving performance in medical imaging tasks. Both models were implemented on FPGAs (field-programmable gate arrays) and graphics processing units (GPUs). DFU_TFNet achieved remarkable results, outperforming models such as AlexNet, VGG16 and GoogleNet. Although FPGAs process a little more slowly than GPUs, their low power consumption makes them especially suitable for portable and real-time diagnostic applications. A more recent study by [PERSON] et al. (2025) [28] presented a practical machine learning method for DFU classification using reinforcement learning.