## 3 Experimental Results and Discussion
### Reliability Performance and Complexity Analysis of Proposed Model  
The reliability performance of the proposed system was validated for 20 consecutive runs as shown in Figure 9, with an accuracy (avg.) as (98.46 \(\pm\) 0.28). We compared the proposed UL-BTD system with the two best-known, as illustrated in Table 10, for the light architecture category using the CE-MRI dataset. Our proposed system has set the new standard by 1.89 to 2.16% accuracy increase, using 18 layers (15 layers when extracting features) of deep architecture with 20 epochs. Additionally, the size of the network was limited to fit within average GPU price hardware resources available for PC category systems. The complexity of the UL-DLA was introduced as a lighter architecture with a lesser number of layers, and SVM was evaluated using Windows 10 Education with a PC machine. Two factors that have been considered for test samples constituting 20% of A-CE-MRI are shown in Table 9, namely tumor detection time per image and GPU memory usage [28]. It is obvious that SVM is a bit costidier as compared to the UL-DLA as a classifier \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Input** & **Tumor** & **TP** & **FN** & **FP** & **TPR** & **PPV** & **F-Measure** & **Acc.** & **Acc.** \\
**Layer** & **Type** & & & & **(Ind.)** & **(Ind.)** & **(Ind.)** & **(Ind.)** & **(Avg.)** \\ \hline \multirow{3}{*}{64 \(\times\) 64} & Glioma & 277 & 8 & 14 & 97.19 & 95.19 & 0.96 & 96.38 & \multirow{3}{*}{97.04} \\  & Meningioma & 125 & 17 & 9 & 88.03 & 93.28 & 0.91 & 95.75 & 97.04 \\  & Pituitary & 184 & 2 & 4 & 98.93 & 97.87 & 0.98 & 98.99 & \\ \hline \multirow{3}{*}{128 \(\times\) 128} & Glioma & 282 & 3 & 10 & 98.95 & 96.58 & 0.98 & 97.86 & \multirow{3}{*}{97.92} \\  & Meningioma & 127 & 15 & 4 & 89.44 & 96.95 & 0.93 & 96.90 & \\  & Pituitary & 185 & 1 & 5 & 99.46 & 97.37 & 0.98 & 99.00 & \\ \hline \multirow{3}{*}{224 \(\times\) 224} & Glioma & 283 & 2 & 3 & 99.30 & 98.95 & 0.99 & 99.18 & \multirow{3}{*}{99.24} \\  & Meningioma & 137 & 5 & 2 & 96.48 & 98.56 & 0.98 & 98.86 & 99.24 \\ \cline{1-1}  & Pituitary & 186 & 0 & 2 & 100.00 & 98.94 & 0.99 & 99.67 & \\ \hline \multirow{3}{*}{256 \(\times\) 256} & Glioma & 278 & 7 & 13 & 97.54 & 95.53 & 0.97 & 96.73 & \multirow{3}{*}{97.60} \\  & Meningioma & 127 & 15 & 7 & 89.44 & 94.78 & 0.92 & 96.41 & 97.60 \\ \cline{1-1}  & Pituitary & 186 & 0 & 2 & 100.00 & 98.94 & 1.00 & 99.66 & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Analysis of input layer size on the proposed model using SVM in the test phase (A-CE-MRI dataset, OvA coding scheme, and polynomial kernel (\(P_{3}\))). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Multi-Class** & **Tumor** & **TP** & **FN** & **FP** & **TPR** & **PPV** & **F-Measure** & **Acc.** & **Acc.** \\
**Scheme** & **Type** & & & **(Ind.)** & **(Ind.)** & **(Ind.)** & **(Ind.)** & **(Ind.)** & **(Avg.)** \\ \hline \multirow{3}{*}{OvA} & Glioma & 277 & 8 & 7 & 97.19 & 97.54 & 0.97 & 97.55 & \multirow{3}{*}{98.26} \\  & Meningioma & 134 & 8 & 8 & 94.37 & 94.37 & 0.94 & 97.39 & 98.26 \\ \cline{1-1}  & Pituitary & 186 & 0 & 1 & 100.00 & 99.47 & 1.00 & 99.83 & \\ \hline \multirow{3}{*}{OvO} & G-M & 279 & 6 & 13 & 97.89 & 95.55 & 96.71 & 95.55 & \multirow{3}{*}{98.45} \\ \cline{1-1}  & M-P & 142 & 0 & 0 & 100.00 & 100.00 & 100.00 & 100.00 & 98.45 \\ \cline{1-1}  & G-P & 285 & 0 & 1 & 100.00 & 99.65 & 99.82 & 99.79 & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of coding schemes: (OvA and OvO) for performance analysis without augmentation (G, M, and P represent glioma, meningioma, and pituitary tumors, respectively, using 224 \(\times\) 224 image size). and takes relatively more time during the test phase. The increased performance, due to generalization improvement, of a multi-class tumor classification system using SVM, is worth making a sacrifice for this minor penalty.