# BTDNet: A Multi-Modal Approach for Brain Tumor Radiogenomic Classification
## 4 Experimental Results
### Ablation Study  
In the following, we perform various ablation experiments to evaluate the contribution of each novel component of BTDNet. First, we utilized different backbone networks to act as the CNN in the 3D analysis component of BTDNet. We experimented with using ResNet50 and ResNet101 (which are larger networks than ResNet18, which was chosen as the CNN in BTDNet), EfficientNetB0 and EfficientNetB3 (which have been used by state-of-the-art methods and proven their value), and ConvNeXt-T [29]. As can be seen in Table 2, ResNet18 is a better backbone CNN than its larger counterparts (ResNet50 and ResNet101). We can see that the larger the ResNet network becomes (and the more layers it has), the lower the performance. This probably occurs due to the small size of the training dataset; larger models are too complex to model. This also occurred in the case of the EfficientNet models, as the performance of EfficientNetB0 surpassed that of EfficientNetB3. Table 2 also illustrates that the performance of ConvNeXt-T surpassed that of ResNet50, which was expected. Finally, the overall best performance was achieved when ResNet18 was utilized. As a second ablation experiment, we utilized different networks to act as the RNN in the 3D analysis component of BTDNet. We experimented with using LSTM and GRU, varying their numbers of units from 64 to 128 and 256. As can be seen in Table 2, LSTM and GRU achieved similar performance. The best results were obtained when LSTM with 128 units was used. If the number of units increased, the performance of the method started to decrease. Next, we assessed the contribution of the routing component and its subcomponents. In more detail, we experimented with not using the routing component at all or without using the mask layer. Thus, our method addressed the problem of different lengths in each volume using a traditional ad hoc strategy, either by removing slices when a larger length was encountered or duplicating slices when the volume contained fewer slices. Table 2 proves that these approaches did not yield good results. Our method outperformed them by large margins (at least 3.3%), as was the case with the state-of-the-art methods that used such ad hoc strategies. This was expected, as the removal of slices resulted in the loss of important information for the final decision, and the duplication of slices negatively affected the final decision, biasing the method toward the repeating data and thus toward one category. We further experimented with using different numbers of hidden units in the \begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & \(\mathbf{F_{1}}\)**Score** \\ \hline stats-EfficientNet [12] & 42.9 \(\pm\) 6.84 \\ YOLO-EfficientNet [9] & 44.4 \(\pm\) 7.41 \\ EfficientNet-Aggr [11] & 46.1 \(\pm\) 4.68 \\ EfficientNet-LSTM-mpbMR [10] & 47.4 \(\pm\) 4.41 \\
3D-Resnet10-Trick [8] & 62.9 \(\pm\) 4.8 \\ \hline
**BTDNet** & **66.2 \(\pm\) 3.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between BTDNet and the state-of-the-art on the RSNA-ASNR-MICCAI BraTS 2021 Challenge dataset. dense layer of the routing component by experimenting with 32, 64, and 128 units. The best results were obtained when we used 64 units. In the following, we assess the contribution of each modality and thus the multimodal approach. Table 2 illustrates the results achieved using our method when employing a unimodal approach (either the FLAIR, T1w, T1 wCE, or T2 modalities) and a multimodal approach (which utilizes all available modalities). One can see in Table 2 that for the unimodal methods, using the FLAIR modality resulted in the best performance. When the T2 modality was used, the second-best performance was achieved. When the T1 wCE modality was used, it outperformed the case when the T1w was used. Table 2 shows that when the multimodal approach was used, it outperformed all unimodal approaches. This was expected, as different modalities provided complementary information.