# Empowering Foot Health: Harnessing the Adaptive Weighted Sub-Gradient Convolutional Neural Network for Diabetic Foot Ulcer Classification

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## Abstract

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## 1. Introduction

- To classify the normal and abnormal DFU images using the proposed AWSg-CNN (Adaptive Weighted Sub-gradient Convolutional Neural Network) for attaining a high prediction rate;
- To deploy a Flask model for easy creation of web applications to predict foot ulcers, thereby displaying remedies in abnormal cases;
- To evaluate the proposed model by comparison with conventional methods for exposing the ideal performance of the model in DFU classification.

#### Paper Organization

## 2. Review of Existing Work

#### Problem Identification

- Existing models have attempted to perform better classification of DFU. In accordance with this, one study [12] utilized a DT model named CTREE and showed a 79.8% accuracy rate; another [13] achieved 97% as an F1-score. The authors of [16] utilized DFINET and showed a 91.9% accuracy rate; a study [21] considered four parallel branches of convolutional layers, showing a 95.8% accuracy rate; another study [22] utilized DFU_VIRNet and showed 0.977 as the accuracy rate. Furthermore, [31] considered a cloud-based DL model and cross-platform mobile application, showing an 87.6% accuracy rate. Despite the attempts of conventional models at DFU classification, there is an opportunity for further improvement.
- With a balanced dataset and enhanced data seizing of DFU, the performance of such methodologies will be enhanced in the future. Furthermore, hyper-parameter optimization of conventional ML and DL methodologies could enhance the performance rate of techniques [20].
- The classification rate and performance of the suggested methodology could be improved by attempting to use different integrations of image-wise and patch-wise classifiers [23].

## 3. Proposed Methodology

#### 3.1. RIW (Random Initialization of Weights)

Algorithm 1. RIW in CNN layer. |

1. Initialize the weights of the convolutional layers randomly using a normal distribution with mean 0 and standard deviation 1 with a specified shape for the weight tensor. 2. Pass the input data through the convolutional layers to generate a set of feature maps. 3. Employ a non-linear activation function, such as ReLU, to the feature maps. 4. Pass the output of the initial convolutional layer to the second convolutional layer. 5. Initialize the weights of the subsequent convolutional layer using a normal distribution with mean 0 and standard deviation 1. 6. To assist in learning the shift of feature space among the first and second convolutional layers, include a bias term to the weights of the second convolutional layer proportional to the mean of the output feature maps from the first convolutional layer. 7. Iterate steps 2–6 for subsequent convolutional layers in the model. 8. Train the model with backpropagation to adjust the weights and biases of the convolutional layers in accordance with the error between the predicted output and the true output. 9. Repeat the training process with varied RIWs to avoid getting stuck in local optima. 10. Assess the model performance on a validation set to determine if it is capable of effectively learning the shift of feature space between the convolutional layers. |

#### 3.2. Log Softmax with ASGO

Algorithm 2: Log Softmax with ASGO. |

// Log Softmax Function $\mathbf{I}\mathbf{n}\mathbf{p}\mathbf{u}\mathbf{t}:\mathrm{A}\mathrm{v}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{o}\mathrm{r}\mathrm{o}\mathrm{f}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{l}\mathrm{n}\mathrm{u}\mathrm{m}\mathrm{b}\mathrm{e}\mathrm{r}\mathrm{s},\mathrm{d}\mathrm{e}\mathrm{n}\mathrm{o}\mathrm{t}\mathrm{e}\mathrm{d}\mathrm{a}\mathrm{s}\mathrm{x}.$ $\mathbf{O}\mathbf{u}\mathbf{t}\mathbf{p}\mathbf{u}\mathbf{t}:\mathrm{A}\mathrm{v}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{o}\mathrm{r}\mathrm{o}\mathrm{f}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{s}\mathrm{a}\mathrm{m}\mathrm{e}\mathrm{s}\mathrm{h}\mathrm{a}\mathrm{p}\mathrm{e}\mathrm{a}\mathrm{s}\mathrm{x},\mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{a}\mathrm{c}\mathrm{h}\mathrm{e}\mathrm{l}\mathrm{e}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{e}\mathrm{p}\mathrm{r}\mathrm{e}\mathrm{s}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{s}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{l}\mathrm{o}\mathrm{g}\mathrm{a}\mathrm{r}\mathrm{i}\mathrm{t}\mathrm{h}\mathrm{m}\mathrm{o}\mathrm{f}\mathrm{t}\mathrm{h}\mathrm{e}$ $\mathrm{s}\mathrm{o}\mathrm{f}\mathrm{t}\mathrm{m}\mathrm{a}\mathrm{x}\mathrm{p}\mathrm{r}\mathrm{o}\mathrm{b}\mathrm{a}\mathrm{b}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{y}.$ $\mathbf{N}\mathbf{o}\mathbf{t}\mathbf{a}\mathbf{t}\mathbf{i}\mathbf{o}\mathbf{n}:\mathrm{L}\mathrm{e}\mathrm{t}\mathrm{f}\left(\mathrm{x}\right)\mathrm{r}\mathrm{e}\mathrm{p}\mathrm{r}\mathrm{e}\mathrm{s}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{l}\mathrm{o}\mathrm{g}\mathrm{s}\mathrm{o}\mathrm{f}\mathrm{t}\mathrm{m}\mathrm{a}\mathrm{x}\mathrm{f}\mathrm{u}\mathrm{n}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{a}\mathrm{p}\mathrm{p}\mathrm{l}\mathrm{i}\mathrm{e}\mathrm{d}\mathrm{t}\mathrm{o}\mathrm{v}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{o}\mathrm{r}\mathrm{x}.$ $\mathrm{f}\left(\mathrm{x}\right)=\mathrm{l}\mathrm{o}\mathrm{g}\left(\mathrm{s}\mathrm{o}\mathrm{f}\mathrm{t}\mathrm{m}\mathrm{a}\mathrm{x}\right(\mathrm{x}\left)\right)$ $\mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e}\mathrm{s}\mathrm{o}\mathrm{f}\mathrm{t}\mathrm{m}\mathrm{a}\mathrm{x}\left(\mathrm{x}\right)\mathrm{i}\mathrm{s}\mathrm{d}\mathrm{e}\mathrm{f}\mathrm{i}\mathrm{n}\mathrm{e}\mathrm{d}\mathrm{a}\mathrm{s}:$ $\mathrm{s}\mathrm{o}\mathrm{f}\mathrm{t}\mathrm{m}\mathrm{a}\mathrm{x}\left({\mathrm{x}}_{\mathrm{i}}\right)=\mathrm{e}\mathrm{x}\mathrm{p}\left({\mathrm{x}}_{\mathrm{i}}\right)/\mathrm{s}\mathrm{u}\mathrm{m}\left(\mathrm{e}\mathrm{x}\mathrm{p}\right({\mathrm{x}}_{\mathrm{j}}\left)\right)\mathrm{f}\mathrm{o}\mathrm{r}\mathrm{a}\mathrm{l}\mathrm{l}\mathrm{j}\mathrm{i}\mathrm{n}\{1,2,...,\mathrm{n}\}$ $\mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{i}\mathrm{s}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{n}\mathrm{u}\mathrm{m}\mathrm{b}\mathrm{e}\mathrm{r}\mathrm{o}\mathrm{f}\mathrm{e}\mathrm{l}\mathrm{e}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{s}\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{v}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{o}\mathrm{r}\mathrm{x}.$ $\mathrm{I}\mathrm{n}\mathrm{p}\mathrm{u}\mathrm{t}\to \mathrm{A}\mathrm{v}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{o}\mathrm{r}\mathrm{o}\mathrm{f}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{l}\mathrm{n}\mathrm{u}\mathrm{m}\mathrm{b}\mathrm{e}\mathrm{r}\mathrm{s}$ $\mathrm{I}\to \mathrm{I}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n},\mathrm{K}\to \mathrm{l}\mathrm{o}\mathrm{g}\mathrm{s}\mathrm{o}\mathrm{f}\mathrm{t}\mathrm{m}\mathrm{a}\mathrm{x}$ $\mathrm{f}\mathrm{o}\mathrm{r}\left(\mathrm{e}:1\to \mathrm{I}\right)\mathrm{d}\mathrm{o}$ $\mathrm{f}\mathrm{o}\mathrm{r}\mathrm{k}:1\to \mathrm{K}\mathrm{d}\mathrm{o}$ $\mathrm{f}\mathrm{o}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{c}\mathrm{h}\mathrm{i}\mathrm{n}\mathrm{p}\mathrm{u}\mathrm{t}{\mathrm{x}}_{\mathrm{j}}^{\mathrm{n}}\mathrm{i}\mathrm{n}\left\{{\mathrm{c}}_{\mathrm{j}}\right\}\mathrm{d}\mathrm{o}$ $\hspace{0.5em}{\mathrm{C}\mathrm{N}\mathrm{N}\mathrm{l}\mathrm{a}\mathrm{y}\mathrm{e}\mathrm{r}\mathrm{x}}_{\mathrm{j}}^{\mathrm{n}}\mathrm{t}\mathrm{h}\mathrm{r}\mathrm{o}\mathrm{u}\mathrm{g}\mathrm{h}\mathrm{C}\mathrm{N}\mathrm{N}\mathrm{t}\mathrm{o}\mathrm{o}\mathrm{b}\mathrm{t}\mathrm{a}\mathrm{i}\mathrm{n}{\mathrm{f}}_{\mathrm{j}}^{\mathrm{n}}$ $\hspace{0.5em}{\mathrm{max}\mathrm{p}\mathrm{o}\mathrm{o}\mathrm{l}\mathrm{r}}_{\mathrm{j},\mathrm{i}}^{\mathrm{n}}=\left(\mathrm{i}-{\mathrm{p}}_{\mathrm{i}}\right){\mathrm{f}}_{\mathrm{j}}^{\mathrm{n}}$ $\mathrm{B}\mathrm{a}\mathrm{t}\mathrm{c}\mathrm{h}\mathrm{N}\mathrm{o}\mathrm{r}\mathrm{m}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{z}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}{\mathrm{d}}_{\mathrm{j},\mathrm{i}}^{\mathrm{n}}$ $\mathrm{C}\mathrm{N}\mathrm{N}\mathrm{l}\mathrm{a}\mathrm{y}\mathrm{e}\mathrm{r}{\mathrm{r}}_{\mathrm{j},\mathrm{i}}^{\mathrm{n}}\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{o}\mathrm{A}\mathrm{G}\mathrm{S}\mathrm{O}$ $\mathrm{u}\mathrm{p}\mathrm{d}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{a}\mathrm{l}\mathrm{l}\mathrm{n}\mathrm{e}\mathrm{t}\mathrm{w}\mathrm{o}\mathrm{r}\mathrm{k}\mathrm{p}\mathrm{a}\mathrm{r}\mathrm{a}\mathrm{m}\mathrm{e}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{s}\mathrm{u}\mathrm{s}\mathrm{i}\mathrm{n}\mathrm{g}\mathrm{log}\mathrm{s}\mathrm{o}\mathrm{f}\mathrm{t}\mathrm{m}\mathrm{a}\mathrm{x}$ $\mathrm{i}\mathrm{f}\mathrm{j}=\mathrm{i}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{n}$ $\hspace{1em}{\mathrm{f}}_{\mathrm{j}}^{\mathrm{n}}$ $\mathrm{e}\mathrm{l}\mathrm{s}\mathrm{e}$ $\frac{1}{{\mathrm{f}}_{\mathrm{j}}^{\mathrm{n}}}$ $\mathrm{e}\mathrm{n}\mathrm{d}$ $\mathrm{e}\mathrm{n}\mathrm{d}$ $\mathrm{e}\mathrm{n}\mathrm{d}$ $\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{c}\mathrm{u}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{i}\mathrm{m}\mathrm{a}\mathrm{g}\mathrm{e}\mathrm{c}\mathrm{l}\mathrm{a}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{s}$ $\mathrm{i}\mathrm{f}\mathrm{i}\mathrm{m}\mathrm{a}\mathrm{g}\mathrm{e}\mathrm{c}\mathrm{l}\mathrm{a}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{s}\mathrm{c}\mathrm{l}\mathrm{a}\mathrm{s}\mathrm{s}\mathrm{i}\mathrm{f}\mathrm{y}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{n}$ $\mathrm{b}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{k}$ $\mathrm{e}\mathrm{n}\mathrm{d}$ $\hspace{1em}{\mathrm{f}}_{\mathrm{j}}^{\mathrm{n}}=\mathrm{l}\mathrm{o}\mathrm{g}\left(\mathrm{s}\mathrm{o}\mathrm{f}\mathrm{t}\mathrm{m}\mathrm{a}\mathrm{x}\right(\mathrm{x}\left)\right)$ $\hspace{1em}\mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e}\mathrm{s}\mathrm{o}\mathrm{f}\mathrm{t}\mathrm{m}\mathrm{a}\mathrm{x}\left(\mathrm{x}\right)\mathrm{i}\mathrm{s}\mathrm{d}\mathrm{e}\mathrm{f}\mathrm{i}\mathrm{n}\mathrm{e}\mathrm{d}\mathrm{a}\mathrm{s}:$ $\hspace{1em}\mathrm{s}\mathrm{o}\mathrm{f}\mathrm{t}\mathrm{m}\mathrm{a}\mathrm{x}\left({\mathrm{x}}_{\mathrm{i}}\right)=\mathrm{e}\mathrm{x}\mathrm{p}\left({\mathrm{x}}_{\mathrm{i}}\right)/\mathrm{s}\mathrm{u}\mathrm{m}\left(\mathrm{e}\mathrm{x}\mathrm{p}\right({\mathrm{x}}_{\mathrm{j}}\left)\right)\mathrm{f}\mathrm{o}\mathrm{r}\mathrm{a}\mathrm{l}\mathrm{l}\mathrm{j}\mathrm{i}\mathrm{n}\{1,2,...,\mathrm{n}\}$ //Adaptive Subgradient Optimizer (Adagrad): $\mathsf{\theta}:\mathrm{M}\mathrm{o}\mathrm{d}\mathrm{e}\mathrm{l}\mathrm{p}\mathrm{a}\mathrm{r}\mathrm{a}\mathrm{m}\mathrm{e}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{s}(\mathrm{w}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}\mathrm{s}\mathrm{a}\mathrm{n}\mathrm{d}\mathrm{b}\mathrm{i}\mathrm{a}\mathrm{s}\mathrm{e}\mathrm{s}).$ $\mathrm{J}\left(\mathsf{\theta}\right):\mathrm{T}\mathrm{h}\mathrm{e}\mathrm{o}\mathrm{b}\mathrm{j}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{e}\mathrm{f}\mathrm{u}\mathrm{n}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{o}\mathrm{b}\mathrm{e}\mathrm{m}\mathrm{i}\mathrm{n}\mathrm{i}\mathrm{m}\mathrm{i}\mathrm{z}\mathrm{e}\mathrm{d}(\mathrm{t}\mathrm{y}\mathrm{p}\mathrm{i}\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{l}\mathrm{y}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{l}\mathrm{o}\mathrm{s}\mathrm{s}\mathrm{f}\mathrm{u}\mathrm{n}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}).$ ${\mathrm{g}}_{\mathrm{t}}:\mathrm{T}\mathrm{h}\mathrm{e}\mathrm{g}\mathrm{r}\mathrm{a}\mathrm{d}\mathrm{i}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{o}\mathrm{f}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{o}\mathrm{b}\mathrm{j}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{e}\mathrm{f}\mathrm{u}\mathrm{n}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{w}\mathrm{i}\mathrm{t}\mathrm{h}\mathrm{r}\mathrm{e}\mathrm{s}\mathrm{p}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{t}\mathrm{o}\mathsf{\theta}\mathrm{a}\mathrm{t}\mathrm{t}\mathrm{i}\mathrm{m}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{e}\mathrm{p}\mathrm{t}.$ $\mathsf{\eta}:\mathrm{L}\mathrm{e}\mathrm{a}\mathrm{r}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{g}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e}(\mathrm{a}\mathrm{h}\mathrm{y}\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{p}\mathrm{a}\mathrm{r}\mathrm{a}\mathrm{m}\mathrm{e}\mathrm{t}\mathrm{e}\mathrm{r}).$ $\mathsf{\epsilon}:\mathrm{A}\mathrm{s}\mathrm{m}\mathrm{a}\mathrm{l}\mathrm{l}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{t}\mathrm{t}\mathrm{o}\mathrm{a}\mathrm{v}\mathrm{o}\mathrm{i}\mathrm{d}\mathrm{d}\mathrm{i}\mathrm{v}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{b}\mathrm{y}\mathrm{z}\mathrm{e}\mathrm{r}\mathrm{o}.$ $\mathrm{G}:\mathrm{A}\mathrm{d}\mathrm{i}\mathrm{a}\mathrm{g}\mathrm{o}\mathrm{n}\mathrm{a}\mathrm{l}\mathrm{m}\mathrm{a}\mathrm{t}\mathrm{r}\mathrm{i}\mathrm{x}\mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{a}\mathrm{c}\mathrm{h}\mathrm{e}\mathrm{l}\mathrm{e}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}{\mathrm{G}}_{\mathrm{i}\mathrm{i}}\mathrm{a}\mathrm{c}\mathrm{c}\mathrm{u}\mathrm{m}\mathrm{u}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{s}\mathrm{q}\mathrm{u}\mathrm{a}\mathrm{r}\mathrm{e}\mathrm{d}\mathrm{s}\mathrm{u}\mathrm{m}\mathrm{o}\mathrm{f}\mathrm{p}\mathrm{a}\mathrm{s}\mathrm{t}\mathrm{g}\mathrm{r}\mathrm{a}\mathrm{d}\mathrm{i}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{s}$ $\mathrm{f}\mathrm{o}\mathrm{r}\mathrm{p}\mathrm{a}\mathrm{r}\mathrm{a}\mathrm{m}\mathrm{e}\mathrm{t}\mathrm{e}\mathrm{r}{\mathsf{\theta}}_{\mathrm{i}}.$
$${\mathrm{G}}_{\mathrm{t}}={\mathrm{G}}_{\left\{\mathrm{t}-1\right\}}+{\mathrm{g}}_{\mathrm{t}}\ast {\mathrm{g}}_{\mathrm{t}}(\mathrm{e}\mathrm{l}\mathrm{e}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}-\mathrm{w}\mathrm{i}\mathrm{s}\mathrm{e}\mathrm{s}\mathrm{q}\mathrm{u}\mathrm{a}\mathrm{r}\mathrm{e}\mathrm{d}\mathrm{s}\mathrm{u}\mathrm{m}\mathrm{a}\mathrm{c}\mathrm{c}\mathrm{u}\mathrm{m}\mathrm{u}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n})$$
$${\mathsf{\theta}}_{\mathrm{t}}={\mathsf{\theta}}_{\left\{\mathrm{t}-1\right\}}-(\mathsf{\eta}/\mathrm{s}\mathrm{q}\mathrm{r}\mathrm{t}({\mathrm{G}}_{\mathrm{t}}+\mathsf{\epsilon}\left)\right)\ast {\mathrm{g}}_{\mathrm{t}}(\mathrm{e}\mathrm{l}\mathrm{e}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}-\mathrm{w}\mathrm{i}\mathrm{s}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{v}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n})$$
Note: The square root is applied element-wise to the matrix G _{t} |

## 4. Results and Discussion

#### 4.1. Dataset Description

#### 4.2. Performance Metrics

#### 4.2.1. Accuracy

#### 4.2.2. Recall

#### 4.2.3. Precision

#### 4.2.4. F1-Score

#### 4.2.5. AUC

#### 4.3. EDA (Exploratory Data Analysis)

#### 4.4. Experimental Results

#### 4.5. Performance Analysis

#### 4.6. Comparative Analysis

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 15.**Analysis in accordance with Metrics [5].

**Figure 16.**Analysis of Metrics [19].

**Figure 17.**Evaluation in accordance with considered Metrics [19].

Accuracy | Recall | Precision | F1-Score | |
---|---|---|---|---|

Proposed Model | 0.99 | 0.99 | 0.99 | 0.99 |

**Table 2.**Analysis of Metrics [5].

Optimization Learning Rate | Accuracy | F1-Score | AUC | Precision | Recall | Error Rate |
---|---|---|---|---|---|---|

Adam_1e-2 | 0.944 | 0.928 | 0.968 | 0.897 | 0.961 | 0.056 |

Adam_1e-3 | 0.922 | 0.902 | 0.95 | 0.869 | 0.937 | 0.077 |

SGD_1e-2 | 0.964 | 0.954 | 0.974 | 0.926 | 0.984 | 0.036 |

SGD_1e-3 | 0.952 | 0.939 | 0.972 | 0.912 | 0.968 | 0.048 |

Adagrad_1e-2 | 0.934 | 0.917 | 0.97 | 0.878 | 0.961 | 0.066 |

Adagrad_1e-3 | 0.925 | 0.907 | 0.968 | 0.865 | 0.953 | 0.075 |

Proposed Model | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.01 |

**Table 3.**Analysis of Metrics [19].

Classifier | Precision | F1-Score | Recall |
---|---|---|---|

DFU_QUTNet | 94.2 | 93.4 | 92.6 |

DFU_QUTNet + KNN | 93.8 | 93.2 | 92.7 |

Existing Model | 95.4 | 94.5 | 93.6 |

Proposed Model | 99 | 99 | 99 |

**Table 4.**Analysis with regard to Metrics [19].

Network | Precision | F1-Score | Recall |
---|---|---|---|

DFUNet | 93.8 | 93.1 | 92.5 |

Existing Model | 95.4 | 94.5 | 93.6 |

Proposed Model | 99 | 99 | 99 |

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## Share and Cite

**MDPI and ACS Style**

Alqahtani, A.; Alsubai, S.; Rahamathulla, M.P.; Gumaei, A.; Sha, M.; Zhang, Y.-D.; Khan, M.A.
Empowering Foot Health: Harnessing the Adaptive Weighted Sub-Gradient Convolutional Neural Network for Diabetic Foot Ulcer Classification. *Diagnostics* **2023**, *13*, 2831.
https://doi.org/10.3390/diagnostics13172831

**AMA Style**

Alqahtani A, Alsubai S, Rahamathulla MP, Gumaei A, Sha M, Zhang Y-D, Khan MA.
Empowering Foot Health: Harnessing the Adaptive Weighted Sub-Gradient Convolutional Neural Network for Diabetic Foot Ulcer Classification. *Diagnostics*. 2023; 13(17):2831.
https://doi.org/10.3390/diagnostics13172831

**Chicago/Turabian Style**

Alqahtani, Abdullah, Shtwai Alsubai, Mohamudha Parveen Rahamathulla, Abdu Gumaei, Mohemmed Sha, Yu-Dong Zhang, and Muhammad Attique Khan.
2023. "Empowering Foot Health: Harnessing the Adaptive Weighted Sub-Gradient Convolutional Neural Network for Diabetic Foot Ulcer Classification" *Diagnostics* 13, no. 17: 2831.
https://doi.org/10.3390/diagnostics13172831