# MSMP-Net: A Multi-Scale Neural Network for End-to-End Monkeypox Virus Skin Lesion Classification
## 4 Experiments
### Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** \\ \hline ResNet50 [20] & \(77.93\pm 6.18\) & \(79.41\pm 7.87\) & \(77.93\pm 6.18\) & \(77.09\pm 6.54\) \\ DenseNet121 [24] & \(58.51\pm 3.34\) & \(59.63\pm 1.58\) & \(58.51\pm 3.34\) & \(57.05\pm 2.17\) \\ MobileNetV3-large [22] & \(80.47\pm 5.65\) & \(82.00\pm 5.62\) & \(80.47\pm 5.65\) & \(79.74\pm 5.77\) \\ CLIP-vit-base [28] & \(72.06\pm 3.13\) & \(73.03\pm 3.96\) & \(72.06\pm 3.13\) & \(69.56\pm 3.64\) \\ CLIP-vit-large [28] & \(83.34\pm 2.30\) & \(84.83\pm 2.35\) & \(83.34\pm 2.30\) & \(82.84\pm 2.50\) \\ Swin-base [21] & \(85.52\pm 2.78\) & \(86.67\pm 2.55\) & \(85.52\pm 2.78\) & \(84.88\pm 2.68\) \\ Swin-small [21] & \(84.75\pm 3.81\) & \(85.60\pm 3.89\) & \(84.75\pm 3.81\) & \(84.34\pm 3.68\) \\ Swin-large [21] & \(84.58\pm 3.98\) & \(84.89\pm 4.09\) & \(84.58\pm 3.98\) & \(83.74\pm 4.27\) \\ ConvNext-tiny [12] & \(82.85\pm 3.87\) & \(83.17\pm 4.28\) & \(82.85\pm 3.87\) & \(82.07\pm 3.93\) \\ ConvNext-small [12] & \(82.52\pm 5.57\) & \(83.11\pm 5.69\) & \(82.52\pm 5.57\) & \(81.89\pm 5.98\) \\ ConvNextV2-tiny [29] & \(82.69\pm 3.32\) & \(83.28\pm 3.66\) & \(82.69\pm 3.32\) & \(81.84\pm 3.55\) \\ VIT-base [26] & \(81.62\pm 1.38\) & \(82.85\pm 1.57\) & \(81.62\pm 1.38\) & \(80.57\pm 1.39\) \\ VIT-large [26] & \(83.91\pm 3.77\) & \(84.67\pm 3.42\) & \(83.83\pm 3.66\) & \(83.20\pm 3.82\) \\ VIT-huge [26] & \(81.62\pm 5.46\) & \(82.40\pm 5.35\) & \(81.62\pm 5.46\) & \(80.78\pm 5.84\) \\ Proposed method & \(87.03\pm 3.43\) & \(87.59\pm 3.37\) & \(87.03\pm 3.43\) & \(86.58\pm 3.66\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification results for different deep learning models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Learning Rate** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** \\ \hline \(1\times 10^{-5}\) & \(83.81\pm 4.40\) & \(84.07\pm 4.69\) & \(83.81\pm 4.40\) & \(83.34\pm 4.57\) \\ \(2\times 10^{-5}\) & \(84.18\pm 3.89\) & \(85.54\pm 3.25\) & \(87.29\pm 2.79\) & \(83.54\pm 4.30\) \\ \(3\times 10^{-5}\) & \(84.86\pm 3.08\) & \(85.45\pm 2.97\) & \(84.86\pm 3.08\) & \(84.48\pm 3.34\) \\ \(4\times 10^{-5}\) & \(87.03\pm 3.43\) & \(87.59\pm 3.37\) & \(87.03\pm 3.43\) & \(86.58\pm 3.66\) \\ \(5\times 10^{-5}\) & \(85.65\pm 2.98\) & \(86.94\pm 2.54\) & \(85.65\pm 2.98\) & \(84.84\pm 2.68\) \\ \(6\times 10^{-5}\) & \(82.67\pm 1.40\) & \(83.17\pm 1.81\) & \(82.67\pm 1.40\) & \(82.07\pm 1.88\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Classification results for different learning rates. There are many methods for monkeypox virus detection, although the dataset or methods used are different. The algorithm proposed in this paper was compared with existing methods, and the results are shown in Table 9. As can be seen from Table 9, the accuracy, precision, recall, and F1-score of this method are 87.03 \(\pm\) 3.43%, 87.59 \(\pm\) 3.37%, 87.03 \(\pm\) 3.43%, and 86.58 \(\pm\) 3.66%, respectively, which are higher than other research methods. This shows that the method in this paper is effective in the diagnosis of monkeypox skin diseases and can have a good classification effect on monkeypox skin images, while providing an objective diagnostic basis for doctors in clinical practice. In order to verify the effectiveness of each strategy and module, we conducted an ablation experiment. The strategies and methods used in this paper mainly include multi-scale feature fusion modules. The experiment mainly verifies the effect of different scale fusion, as shown in Table 10. As can be seen from Table 10, the effect of fusing four scale features is better than that of other scale feature fusion.