## 3 Experimental Results and Discussion
### Contribution and Implications  
We presented an intelligent Ultra-Light Deep Learning Architecture (UL-DLA) to represent learning-based features along with textural features predicting MRI brain tumor type with the help of SVM. The main focus of the framework is to support intraoperative brain surgery support by reducing the overall time to the prediction stage (Section 3.4). The Discrete Wavelet Transforms (DWT) based analysis of MRI images was carried out for contrast enhancement and downsampling of 512 \(\times\) 512-sized MRI images. The challenging MRI dataset of brain tumors suffered from variations in class sizes. The class imbalance was addressed by using multi-facet augmentation. The sensitivity analysis of different classifiers for the proposed framework concluded that SVM classified better than the k-NN and RF, as well as the SoftMax classifier. The input MRI scans' analysis established 224 \(\times\) 224 sized images as the optimum choice. As far as the choice of coding schemes is concerned, out of the two multi-class coding schemes (i.e., One-Versus-One (OvO) and One-Versus-All (OvA)), OvO was found better than OvA for the proposed framework. The complexity analysis of the proposed system laid down a simple deep learning-based automated system with an overall prediction time of 11.69 ms per MRI test image compared to 15 ms per image reported by [14]. A comparison with recent techniques, using the same dataset, was presented for performance analysis, including transfer learning and fine-tuned architectures in the concluding section.  
Our study has some limitations suggested as future research directions. First, the proposed framework needs the general clinical trial in the second phase for resolving the patients' data as the second opinion in addition to the expert opinion. Second, the prediction phase or decision making in deep learning-based strategies is complex and opaque where accuracy massively depends on huge parametric space using efficient algorithms. From the XAI point of view [55; 56], the proposed deep learning architecture, UL-DLA, may be analyzed with a transparent white-box for multimodal and multi-center data fusion.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Model/Study** \\ **Reference** \\ \end{tabular} } & \begin{tabular}{c} **k-Fold Cross-** \\ **Validation/Dataset** \\ **Partitioning** \\ \end{tabular} & \begin{tabular}{c} **Augmentation/** \\ **Smote** \\ \end{tabular} & \begin{tabular}{c} **Classification** \\ **Technique** \\ \end{tabular} & \begin{tabular}{c} **Precision** \\ **(\%Avg.)** \\ \end{tabular} & \begin{tabular}{c} **Recall** \\ **(\%Avg.)** \\ \end{tabular} & \begin{tabular}{c} **F-Score** \\ **(\%Avg.)** \\ \end{tabular} & \
\begin{tabular}{c} **Accuracy** \\ **(\%Avg.)** \\ \end{tabular} \\ \hline [52] & 10-fold & \(\times\) & nLBP + \(k\)-NN & \(\times\) & \(\times\) & 95.56 \\ \hline [25] & 5-fold; 10-fold & \(\surd\) & CNN & 95.79 & 96.51 & 96.11 & 96.56 \\ \hline [32] & 5-fold; 10-fold & \(\surd\) & Multi-scale CNN & \(\times\) & 96.67 & \(\times\) & 97.30 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Proposed \\ framework \\ \end{tabular} } & 10-fold; 5 fold for & \multicolumn{3}{c}{UL-DLA +} & & & \\  & limited & \(\surd\) & GLCM + SVM & 97.07 & 97.30 & 97.20 & 99.24 \\ \cline{1-1}  & experiments & \multicolumn{3}{c}{(UL-BTD system)} & & & \\ \hline \hline \end{tabular}
\end{table}
Table 12: _Cont._