# DeepSarc-US: A Deep Learning Framework for Assessing Sarcopenia Using Ultrasound Images
## 3 Results
### Classification of QMT  
This section summarizes the results of the classification tasks. As previously explained, the _RW-Classification_ models were initialized with the weights of our regression training runs; however, the _IV-Classification_ models were initiated with publicly available weights and were pretrained on the ImageNet dataset. The classification accuracy results are shown in Table 3. Among the _IV-Classification_ models, SwinT-L showed the best accuracy of 43.84%, and among the _RW-Classification_ models, ViT-L showed the best accuracy of 43.84%. It was observed that the _IV-Classification_ models showed better performances compared to the _RW-Classification_ models. SwinT-B and SwinT-L were the only models that showed improvements in their _RW-Classification_ compared to _IV-Classification_ models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Median Absolute Error (cm)**} \\ \hline
**Model** & _IV-Regression_ & _CW-Regression_ & **Delta** \\ \hline ResNet101 & 0.40 & 0.30 & \(+0.10\uparrow\) \\ \hline DensNet121 & 0.50 & 0.38 & \(+0.12\uparrow\)* \\ \hline ConvNext-B & **0.27** & **0.23** & \(+0.04\uparrow\) \\ \hline ViT-B & 0.31 & 0.27 & \(+0.03\uparrow\)* \\ \hline ViT-L & 0.32 & 0.29 & \(+0.03\uparrow\) \\ \hline MAE-B & 0.34 & 0.31 & \(+0.03\uparrow\) \\ \hline MAE-L & 0.28 & 0.28 & 0.00 \\ \hline SwinT-B & **0.27** & 0.24 & \(+0.03\uparrow\)* \\ \hline SwinT-L & 0.30 & **0.25** & \(+0.05\uparrow\)* \\ \hline All & 0.28 & 0.25 & \(+0.03\uparrow\)** \\ \hline \hline \end{tabular}  
* and ** denote statistically significant differences with \(p\)-values of \(<\)0.05 and \(p\)-value \(<0.01\), respectively, between _IV-Regression_ models with ImageNet weights and _CW-Regression_ models with corresponding classification weights. \(\uparrow\): _CW-Regression_ outperformed _IV-Regression_; Results of ConvNext-B model are in bold representing the minimum error for CNN-based models. The bold values for SwinT-B and SwinT-L represent the minimum errors in ViT-based models.  
\end{table}
Table 2: Median absolute error of QMT estimations.  
The activation maps from _Classification_ models for ResNet101, DensNet121, ViT-B, and MAE-B are shown in Figures 5 and 6. Based on the achieved activation maps, it was discovered that in both the _IW-Classification_ and _RW-Classification_ models, the activation maps in the ViT-B and MAE-B models mainly focused on detecting the edge of the femur bone or the skeletal muscle tissue itself in most of the correctly classified US images, as shown in Figure 5c,d. There were no distinguishing patterns in the activation maps between the _IW-Classification_ and _RW-Classification_ models. Furthermore, no discernible differences in the activation maps of ViT-B against ViT-L or MAE-B versus MAE-L were found; thus, only the results for ViT-B and MAE-B are provided. The activation maps for two sample test subjects are shown in the first row for correctly classified cases and in the second row for misclassified cases. It is important to note that the activation maps were not relevant in some misclassified cases of CNN-based models, especially when the classification errors were greater than two classes, as shown in Figure 5e,f.  
Figure 5: Activation maps in classification models: ResNet101 (**a,e**), DensNet (**b,f**), ViT-B (**c,g**), and MAE-B (**d,h**). The first and second rows represent activation maps for two different subjects. The first row was correctly classified, and the second row was misclassified. (GT: ground truth class label, Pred: predicted class label).  
Figure 6: Sample activation maps of (**a**) ViT-B and (**b**) MAE-B, detecting the body of the muscle.