## 1 Introduction
### Related Studies
It integrates compositional pattern production networks (CPPNs) to recognise structured and unstructured images, SVM for classification, hierarchical clustering to group data and ELM with a single hidden layer for fast classification. This research evaluates several deep learning architectures, including AlexNet, VGG16, GoogLeNet and ResNet50, to classify DFUs. The dataset is DFU2020, and ResNet50 achieved the highest classification accuracy (99.49% for ischaemia). To improve performance with limited datasets, affine transformation techniques were used to augment the data. The results show significant improvements in classification efficiency. Analysis of the clustering scenarios reveals that the model's performance varies according to the severity of the ulcer, managing to effectively distinguish between four groups: Group 1--Mild to moderate localised cellulitis; Group 2--Moderate to severe cellulitis; Group 3--Moderate to severe cellulitis with ischaemia; and Group 4--Life- or limb-threatening infections. Table 1 summarises the studies found in the literature that use neural networks to classify DFUs, while Table 2 summarises the datasets available for training the models. Despite the reported impact of skin tones in ulcer assessment [29; 30], none of the reviewed studies has addressed this factor, neither during dataset collection or analysis, nor during the prediction model development. In general, VGG16 is the most widely used architecture, while other models such as InceptionV3, ResNet50, InceptionResNetV2, MobileNetV2 and EfficientNet are also explored. Regarding performance, the results are mostly reported in terms of accuracy, precision and F1-score. The EfficientNet model shows the best average results, with accuracy up to 99.39% for ischaemia and 97.92% for infection, while VGG16 has a solid performance, ranging from 83.36% to 100% accuracy in the studies analysed. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** & **Methodology** & **\# Images** & **Performance (\%)** \\ \hline [PERSON] et al. (2019) [19] & VGG16 & 1639 & Accuracy: 83.36 \\ [PERSON] et al. (2020) [20] & MobileNetv2, VGG16 & 1109 & F1-score: 94.05 \\ [PERSON] et al. (2020) [21] & DFU-QUTNet, SVM, KNN & 754 & Precision: 95.40 \\ [PERSON] et al. (2021) [22] & VGG16, ResNet, EfficientNet & 15,683 & Precision: 57.3; F1-score: 55.2 \\ [PERSON] et al. (2022) [23] & EfficientNet & 58,200 & Accuracy Ischaemia: 99.39 \\ [PERSON] et al. (2022) [24] & VGG16, VGG19 & 1088 & Precision: 100 \\ [PERSON] et al. (2023) [25] & AWSg CNN & 493 & Accuracy, F1-score, AUC: 99 \\ [PERSON] et al. (2023) [26] & DRNN, PFCNN & 15,683 & Accuracy: 99.32 \\  & & & AlexNet: Accuracy: 89.11; F1-score: 88.1 \\  & & & VGG16: Accuracy: 90.37; F1- score: 90.9 \\ [PERSON] et al. (2024) [27] & DFU\_FNet, DFU\_TFNet & 493 & GoogleNet: Accuracy: 91.93; F1-score: 92.9 \\  & & & DFU\_FNet + SVM: Accuracy: 94.71; F1-score: 94.5 \\  & & & Classification accuracy: 93.75 \\ [PERSON] et al. (2025) [28] & RL, CPPN, SVM, ELM, ResNet50 & 4000 & Cluster 1 Efficiency: 71–88 \\  & & & Cluster 2 Efficiency: 85–97 \\  & & & Cluster 3 Efficiency: 90–98 \\  & & & Cluster 4 Efficiency: 93.5–98.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of studies found in the literature.