## 3 Experimental Outcome and Discussions
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Dataset** & **Classifier** & **Modality** & **TP** & **FN** & **TN** & **FP** & **ACC** & **PRE** & **SEN** & **SPE** & **F1S** & **NPV** \\ \hline \multirow{8}{*}{BRATS} & \multirow{4}{*}{DT} & Flair & 388 & 12 & 589 & 11 & 97.70 & 97.24 & 97.00 & 98.17 & 97.12 & 98.00 \\  & & T2 & 385 & 15 & 586 & 14 & 97.10 & 96.49 & 96.25 & 97.67 & 96.37 & 97.50 \\  & & T1C & 383 & 17 & 581 & 19 & 96.40 & 95.27 & 95.75 & 96.83 & 95.51 & 97.16 \\  & & Flair & 387 & 13 & 591 & 9 & 97.80 & 97.73 & 96.75 & 98.50 & 97.24 & 97.85 \\  & & T2 & 384 & 16 & 588 & 12 & 97.20 & 96.96 & 96.00 & 98.00 & 96.48 & 97.35 \\  & & T1C & 385 & 15 & 589 & 11 & 97.40 & 97.22 & 96.25 & 98.17 & 96.73 & 97.52 \\  & & Flair & 392 & 8 & 592 & 8 & 98.40 & 98.00 & 98.00 & 98.67 & 98.00 & 98.67 \\  & & SVM-Linear & T2 & 391 & 9 & 590 & 10 & 98.10 & 97.50 & 97.75 & 98.33 & 97.63 & 98.50 \\  & & T1C & 384 & 16 & 587 & 13 & 97.10 & 96.72 & 96.00 & 97.83 & 96.36 & 97.34 \\  & & Flair & 395 & 5 & 596 & 4 & 99.10 & 99.00 & 98.75 & 99.33 & 98.87 & 99.17 \\  & & SVM-RBF & T2 & 394 & 6 & 595 & 5 & 98.90 & 98.74 & 98.50 & 99.17 & 98.62 & 99.00 \\  & & T1C & 389 & 11 & 588 & 12 & 97.70 & 97.01 & 97.25 & 98.00 & 97.13 & 98.16 \\ TCIA & SVM-RBF & T2 & 393 & 7 & 391 & 9 & 98.00 & 97.76 & 98.25 & 97.75 & 98.01 & 98.24 \\ Clinical & SVM-RBF & T2+Flair & 395 & 5 & 194 & 6 & 98.17 & 98.50 & 98.75 & 97.00 & 98.63 & 97.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance values obtained with the proposed VGG19 architecture. Figure 4: Sample outcome attained with customized VGG19. Figure 5: The area under the curve (AUC) attained for VGG19 with SVM-RBF classifier. The performance of the proposed DLA is then validated with other methods existing in the literature, and the results are presented in Table 5. These results confirm that customized VGG19 offers enhanced outcomes for the BRATS, TCIA and clinical level images compared to the results of existing methods. This study was focused on implementing a DLA to segregate MRI slices into normal/tumor classes, and after the segregation, the MRI slices with the tumor were further examined by the doctor. In future,  
Figure 6: Classification results attained with customized VGG19 with SVMRBF. Figure 7: Graphical representation of the performance measures attained with customized VGG19. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Approach** & **Accuracy (\%)** \\ \hline \hline \multirow{4}{*}{[PERSON] et al. [11]} & \multirow{4}{*}{Machine learning with fused features} & 97 (SVM) \\  & & 98 (Naive-Bayes) \\  & & 86 (Ensemble) \\ \cline{3-3}  & & 97 (DT) \\ \cline{3-3}  & & 97 (KNN) \\ \cline{3-3}  & & 89 \\ \cline{3-3}  & & 97.8 (SoftMax) \\ \cline{3-3}  & & 93.6 (MSVM) \\ \cline{3-3}  & & 92.2 (KNN) \\ \cline{3-3}  & & 94.4 (Ensemble) \\ \cline{3-3}  & & 97.38 \\ \cline{3-3}  & & Convolutional neural networks & 91.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance validation of proposed VGG19 with the existing methods. the outcome of the proposed system along with the clinically collected data can be used to develop a computerized model to track the ependymal tumor dissemination. The future scope of the proposed research includes:  
* Enhancing the handcrafted feature vector by considering the additional texture and shape features. * Adjusting the fully-connected and drop-out layers to improve the categorization accuracy. * Improving the feature-concatenation technique to attain better results. * Implementing the proposed VGG19 DLA to classify the tumors into low/high grade gliomas. * Developing a neural-network model for ependymal tumor dissemination.