## 2 Materials and Methods  
### Datasets  
This work made use of two publicly available datasets: the DFU dataset and the DFUC2021 dataset [22]. Both datasets consist of images of diabetic foot ulcers, annotated by medical professionals, and were combined and adapted to create a more comprehensive dataset for model training and evaluation. The DFU dataset was collected over a period of five years at Lancashire Teaching Hospitals. The images were captured after the debridement process, which involves removing necrotic and devitalized tissue. Three different camera models were used to capture these images: the Kodak DX4530, the Nikon D3300, and the Nikon COOLPIX P100. Each image in the DFU dataset was annotated by two healthcare professionals, a consultant physician and a diabetic foot specialist. In cases where their assessments differed, the more experienced clinician made the final decision [33, 34]. The DFU dataset is structured around two binary classification tasks: distinguishing between ulcers with and without infection, and between ulcers with and without ischaemia. To increase the variability and size of the dataset, natural data augmentation techniques were applied. For the infection class, this involved including both the original image and enlarged versions of the same lesion. For the ischaemia class, original, rotated and mirrored versions of the ulcers were included [33, 34]. The second dataset used in this study is the DFUC2021 dataset, which is organised into four distinct categories: \"non-existent,\" which includes images of healthy skin, healing ulcers, or ulcers without infection or ischaemia; \"infection,\" which includes ulcers showing only signs of infection; \"ischaemia,\" which includes ulcers showing only signs of ischaemia; and \"both,\" which includes ulcers exhibiting both infection and ischaemia [22]. Since the two datasets differ in their class structures, it was necessary to adapt the DFU dataset to match the four-class schema of the DFUC2021 dataset. This involved reclassifying and restructuring the DFU data so that all images could be consistently labeled under the categories non-existent, infection, ischaemia or both. After this harmonization process, the final dataset used to train the model consisted of 1039 (46.7%) images of feet with ulcers and 1185 (53.3%) images of feet with healthy skin or no reported problem. In addition, the dataset quality was also a matter of concern and observation. As many technologies, particularly those based on computer vision, have shown inconsistent results when applied to individuals with specific skin tones, it is essential to ensure that AI systems perform accurately and fairly across all skin tones. This issue is deeply ethical. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Name** & **Images Available** & **Collection Conditions** & **Resolution (Pixels)** & **Open-Acess** \\ \hline DFUC 2020 [31] & 4000 (2496 training and 2097 test) & \begin{tabular}{l} Captured by digital camera; variations \\ in distance, angle, orientation, \\ lighting, focus and presence of \\ background objects \\ Three cameras used: Kodak DX4530, \\ Nikon D3300 and Nikon \\ COOLPIX P100 \\ Used a Samsung Galaxy Note 8 and \\ an iPad \\ Database [32] \\ Diabetic Foot Ulcer [33] \\ \end{tabular} & \begin{tabular}{l} 640 \(\times\) 480 \\ \end{tabular} & No \\ \hline \begin{tabular}{l} DFUC 2021 [22] \\ 5734 fabric the test set) \\ \end{tabular} & \
\begin{tabular}{l} No \\ Nikon D3300 and Nikon \\ COOLPIX P100 \\ Used a Samsung Galaxy Note 8 and \\ an iPad \\ Not specified \\ Captured by digital camera \\ \end{tabular} &
\begin{tabular}{l} 640 \(\times\) 480 \\ \end{tabular} & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of foot ulcers datasets. Systems that underperform for certain racial or ethnic groups can reinforce inequalities and result in serious real-world consequences, such as misdiagnoses in healthcare [35].