# MSMP-Net: A Multi-Scale Neural Network for End-to-End Monkeypox Virus Skin Lesion Classification
## 4 Experiments
### Experimental Results and Analysis  
First, the cross-validation technique is used to evaluate the test accuracy of the model. The entire dataset is divided into k subsets of equal size. Then, k \(-\) 1 subsets are used for training, and the remaining subset is used to test the model. Each subset of the data is used for either training or validation. After using all the data subsets, the cumulative average validation score is calculated. The MSLD v2.0 dataset has five folders, each containing three sub-files, namely the training dataset, validation dataset, and test dataset. Class imbalance is crucial to prevent bias in the model and ensure reliable performance across all classes. Data augmentation is used to address class imbalance. The MSLD v2.0 dataset consists of two subsets: the original dataset and augmented dataset, as shown in Table 2. Several data augmentation methods, including rotation, translation, reflection, shear, hue, saturation, contrast and brightness jitter, noise, and scaling, were applied to the original dataset. This augmentation process helped to mitigate the class imbalance by increasing the sample sizes of minority classes (e.g., cowpox and measles) to be more comparable with the majority classes. We conducted experiments to compare the model's performance on the original dataset versus the augmented dataset. The experimental results using the cross-validation technique are shown in Tables 4 and 5. The accuracy, precision, recall, and F1-score on the original dataset are 79.45 \(\pm\) 5.66%, 81.03 \(\pm\) 4.83%, 79.45 \(\pm\) 5.66, and 78.85 \(\  
In order to further verify the classification effect of the algorithm proposed in this paper, the classification effects of different models are compared under the same dataset. The models used are ResNet50 [20], VIT [26], DenseNet121 [27], MobileNetV3 [22], CIIP [28], Swin-Transformer [21], and ConvNext [12]. The evaluation indicators used are accuracy, precision, recall, and F1-score. The experimental results are shown in Table 6. As can be seen from Table 6, the algorithm proposed in this paper is better than the classification effect of other models, which further shows that the method in this paper can effectively diagnose monkeybox skin images. The bold value represents the highest value in each indicator. To further validate the proposed model, this paper also trained and tested the dataset on various learning rates and batch sizes. In this experiment, the learning rate was set to \(1\times 10^{-5}\), \(2\times 10^{-5}\), \(3\times 10^{-5}\), \(4\times 10^{-5}\), \(5\times 10^{-5}\), and \(6\times 10^{-5}\), and the batch sizes were \(4\), \(8\), \(16\), and \(32\). After using these parameters to train our proposed model, all the obtained results are shown in Tables 7 and 8. From Table 7, it can be seen that the learning rate of \(4\times 10^{-5}\) exceeds the other learning rates. The \(4\times 10^{-5}\) learning rate used produces the best results with the highest accuracy. After studying the learning rate, each batch size was evaluated, and a learning rate of \(4\times 10^{-5}\) was used for evaluation. During the evaluation, the F1-score, precision, recall, and accuracy of all batch sizes were compared. From Table 8, it can be seen that the batch size of \(16\) performs well, with an accuracy of \(87.03\pm 3.43\%\), while the batch size of \(32\) performs correspondingly better, but not as well as the batch size of \(16\).