# Resizer Swin Transformer-Based Classification Using sMRI for Alzheimer's Disease
## 4 Evaluation
### Ablation Experiments  
We carried out several ablation experiments to maximize the experimental outcomes.  
Experiment 1: We used sMRI slices in three different orientations. In Table 3, the experimental findings are displayed. The worst result was obtained when using the coronal plane slice without the skull-stripping preprocessing procedure, which differed greatly from the findings of the other two orientations. Since most experiments were centered on the axial plane and the differences between the experimental outcomes in the axial and sagittal planes were not particularly apparent, axial plane slices were employed in this experiment.  
Experiment 2: Tests were run to show how well the various parts of our proposed RST model worked together. The test results are listed in Table 4. The RST model, as implemented by a CNN, performs noticeably better than the other experiments, as shown in the table. In contrast, the step of skull stripping only slightly improves the performance of the RST model and is not proportional to the additional expense it incurs. We used sMRI's axial, sagittal, and coronal slices as the three input channels of the RST model, but surprisingly, the RST model did not perform well on 2.5D image data. Our analysis results may be due to 2.5D images containing more information, and the model cannot effectively extract features from them. At the same time, we also conducted a comparison with the Swin Transformer, and the experimental results show that the RST is still superior to the Swin Transformer on the ADNI dataset.  
Experiment 3: Investigations were carried out on several datasets to confirm the robustness of our proposed model. The outcomes of our trials are shown in Figure 6. The table illustrates that our model performs better for various datasets. The analysis results may be caused by the unevenness of the datasets and the image discrepancies between the datasets because the findings of AIBL, on the other hand, are worse than those of ADNI.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Types**} & \multicolumn{4}{c}{**Classification Results**} \\ \cline{3-6}  & & **ACC\%** & **SEN\%** & **SPE\%** & **PRE\%** \\ \hline DenseCNN [12] & ROI & 89.80 & 98.50 & 85.20 & – \\ CNN [17] & Whole & 93.00 & 92.00 & 94.00 & – \\ LDMIL [19] & Patch & 92.02 \(\pm\) 0.93 & 90.76 \(\pm\) 2.72 & 92.40 \(\pm\) 1.10 & – \\ ResNet+Attention [20] & Attention & 90.00 & 92.80 & 87.50 & – \\ ResNet+ViT [22] & Transformer & 92.26 & 88.98 & 94.04 & – \\ CNN+ViT [23] & Transformer & 90.58 & – & – & – \\ CNN+ViT [24] & Transformer & 96.80 & – & – & 97.20 \\
**Ours** & **Transformer** & **99.59** & **99.58** & **99.59** & **99.83** \\ \hline \hline \end{tabular} The abbreviations of the models are taken from the corresponding research. ROI means the model is based on the region of interest (ROI), and the rest are similar. Accuracy (ACC), sensitivity (SEN), specificity (SPE), and precision (PRE) are the four indicators used to assess the classification performance. The bold denotes our method.  
\end{table}
Table 2: Comparison of our proposed model with related studies.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Types**} & \multicolumn{4}{c}{**Classification Results**} \\ \cline{2-5}  & **ACC\%** & **SEN\%** & **SPE\%** & **PRE\%** \\ \hline Sagittal & 99.69 & 99.74 & 99.67 & 99.54 \\ Coronal & 99.07 & 99.46 & 98.79 & 98.50 \\ Axial & 99.59 & 99.58 & 99.59 & 99.83 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparisons of three directional slices using RST model.