## 3 Experimental Outcome and Discussions
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Network** & **Modality** & **TP** & **FN** & **TN** & **FP** & **ACC** & **PRE** & **SEN** & **SPE** & **F1S** & **NPV** \\ \hline \multirow{4}{*}{AlexNet} & Flair & 377 & 23 & 581 & 19 & 95.80 & 95.20 & 94.25 & 96.83 & 94.72 & 96.19 \\  & T2 & 368 & 32 & 573 & 27 & 94.10 & 93.16 & 92.00 & 95.50 & 92.58 & 94.71 \\  & T1C & 361 & 39 & 568 & 32 & 92.90 & 91.86 & 90.25 & 94.67 & 91.05 & 93.57 \\  & Flair & 379 & 21 & 585 & 15 & 96.40 & 96.19 & 94.75 & 97.50 & 95.47 & 96.53 \\ VGG16 & T2 & 364 & 36 & 570 & 30 & 93.40 & 92.39 & 91.00 & 95.00 & 91.69 & 94.06 \\  & T1C & 368 & 32 & 564 & 36 & 93.20 & 91.09 & 92.00 & 04.00 & 91.54 & 94.63 \\  & Flair & 381 & 19 & 584 & 16 & 96.50 & 95.97 & 95.25 & 97.33 & 95.61 & 96.85 \\ VGG19 & T2 & 378 & 22 & 581 & 19 & 95.90 & 95.21 & 94.50 & 96.83 & 94.86 & 96.35 \\  & T1C & 370 & 30 & 574 & 26 & 94.40 & 93.43 & 92.50 & 95.67 & 92.96 & 95.03 \\  & Flair & 371 & 29 & 567 & 33 & 93.80 & 91.83 & 92.75 & 94.50 & 92.29 & 95.13 \\ ResNet50 & T2 & 358 & 42 & 565 & 35 & 92.30 & 91.09 & 89.50 & 94.17 & 90.29 & 93.08 \\  & T1C & 362 & 38 & 566 & 34 & 92.80 & 91.41 & 90.50 & 94.33 & 90.95 & 93.71 \\  & Flair & 374 & 26 & 583 & 17 & 95.70 & 95.65 & 93.50 & 97.17 & 94.56 & 95.73 \\ ResNet101 & T2 & 361 & 39 & 568 & 32 & 92.90 & 91.85 & 90.25 & 94.67 & 91.05 & 93.57 \\  & T1C & 366 & 34 & 565 & 35 & 93.10 & 91.27 & 91.50 & 94.17 & 91.39 & 94.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance measures attained with conventional DLA with SoftMax classifier. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Classifier** & **Modality** & **TP** & **FN** & **TN** & **FP** & **ACC** & **PRE** & **SEN** & **SPE** & **F1S** & **NPV** \\ \hline \multirow{4}{*}{DT} & Flair & 379 & 21 & 587 & 13 & 96.60 & 96.68 & 94.75 & 97.83 & 95.71 & 96.55 \\  & T2 & 377 & 23 & 584 & 16 & 96.10 & 95.93 & 94.25 & 97.33 & 95.08 & 96.21 \\  & T1C & 368 & 32 & 571 & 29 & 93.90 & 92.69 & 92.00 & 95.17  
Table 4 shows the overall performance measures achieved after a 10-fold cross validation for the BRATS, TCIA and clinical brain MRI datasets, and averages of the performance measures are considered for the validation. The results shown in this table confirm that the proposed network helped to achieve better classification accuracy for all the considered datasets irrespective of the modalities of the test images. Figure 6 depicts the sample classification result attained with the proposed VGG19 with SVM-RBF classifier for the TCIA database. The TCIA database consists of the brain MRI slices with the skull section, and hence, the average detection accuracy attained for the TCIA database is less compared with the results attained with the BRATS database. Figure 7 depicts the overall results attained with the customized VGG19 for the considered test images, and these results confirm that the VGG19 works well on the considered image dataset. Further, this architecture helped to attain classification accuracy of 98.17%. This outcome confirmed that the proposed DLA is clinically significant, and in future, it can be considered to detect tumors in clinical-grade brain MRI slices. During the real time implementation, this DLA can be used as an assisting tool for the doctor to make the possible decisions during brain tumor detection and treatment-planning processes.