## 1 Introduction
### Related Studies  
The classification of DFUs using automated methods is a critical area of research in medical imaging. These tools have the potential to improve early diagnosis and reduce severe complications like infections or amputations, especially in diabetic patients. While various machine learning approaches, such as attention-based models, Vision Transformers (ViTs) and custom-designed architectures, have been explored, the use of such systems in clinical settings requires robust tools [17]. Standard Convolutional Neural Networks (CNNs) like ResNet and VGG are widely used models that, besides their proven performance, also have associated explainability algorithms that support trust in the algorithms behaviour. For the current system, CNNs have been selected for DFU classification also due to their ability to benefit from efficient transfer learning techniques, interpretability and computational feasibility. These advantages are particularly relevant when addressing challenges like skin colour variability, which can alter the visual characteristics of ulcers across diverse patient populations, impacting model generalisability. In fact, according to a review by [PERSON] et al. [18], 'advanced deep learning approaches have proven to be more promising than ML approaches. The CNN-based solutions (...) have dominated the problem domain'. This section reviews related work on CNN-based DFU classification, highlighting the strengths of fine-tuned models, while also considering alternative approaches and their limitations. Additionally, efforts to incorporate skin colour diversity in medical imaging are examined to ensure equitable diagnostic outcomes, with the purpose of setting the foundation for the proposed CNN-based framework tailored for robust and inclusive DFU classification. When surveying recent and highly cited literature, there are promising early approaches that explore beyond hand-crafted feature-based approaches. However, the lack of publicly available supporting datasets prevents comprehensive comparisons. For example, in the work by [PERSON] et al. [19], a custom architecture bilinear CNN is used to explore depth and granulation classification, which are crucial indicators of healing. A new dataset was collected without specific ambient control or class balancing. Before being fed into the network, the images underwent preprocessing steps like wound area extraction, sharpening, resizing, and augmentation. Using holdout validation, the proposed approach achieving an accuracy of 84.6%. Also, in [PERSON] et al. [20], a few-shot DFU classification method is proposed. By augmenting a limited dataset of DFU images, specifically collected, and fine-tuning a pre-trained ResNet50 model, the researchers achieved a high average accuracy of 98.67%. In [PERSON] et al. [21] the authors introduce a new dataset of 754 foot images categorized as healthy or containing DFUs. A novel network, named DFU_QUTNet, focused on increasing network width to improve gradient propagation and combine features effectively. The feature-based approach relied on Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers that achieved an F1-score of 94.5%, outperforming other CNNs like GoogleNet, VGG16 and AlexNet on this task. The public release of the DFU2021 dataset [22] came to establish a common benchmarking reference and increased the quality of existing research. In [PERSON] et al. [22] a DFU classification study has as its main objective identifying infection and ischaemia. The authors explored several pre-trained models, such as VGG16, ResNet101 and EfficientNetB0, tested with DL models. The best performance was obtained with EfficientNetB0, which, after adjustments and data augmentation, achieved an output with a precision of 0.57, recall of 0.62 and F1-score of 0.55, excelling in the classification of ischaemia.