## 4 Performance Validation
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Labels** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F-Score** \\ \hline
**Training Phase (70\%)** & & & & & \\ Meningioma & 99.21 & 97.43 & 99.20 & 99.21 & 98.31 \\ Pituitary & 99.16 & 98.92 & 98.32 & 99.53 & 98.62 \\ Cioma & 98.83 & 98.99 & 98.49 & 99.13 & 98.74 \\
**Average** & **99.07** & **98.45** & **98.67** & **99.29** & **98.55** \\
**Testing Phase (30\%)** & & & & & \\ Meningioma & 99.02 & 96.33 & 99.53 & 98.87 & 97.90 \\ Pituitary & 99.13 & 99.63 & 97.45 & 99.84 & 98.53 \\ Glioma & 99.02 & 99.08 & 98.85 & 99.18 & 98.96 \\
**Average** & **99.06** & **98.34** & **98.61** & **99.30** & **98.46** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Result analysis of EADL-BTMIC technique with various measures on 70% of TR and 30% of TS data. Figure 6: Result analysis of EADL-BTMIC technique on 70% of TR and 30% of TS data. A brief precision-recall examination of the EADL-BTMIC model on the test dataset is portrayed in Figure 8. By observing the figure, it is noticed that the EADL-BTMIC model has accomplished maximum precision-recall performance under all classes. Figure 8: Precision-recall curve analysis of EADL-BTMIC technique. Figure 7: Accuracy and loss analysis of EADL-BTMIC technique (**a**) 80:20 of TR/TS accuracy, (**b**) 80:20 of TR/TS loss, (**c**) 70:30 of TR/TS accuracy, and (**d**) 70:30 of TR/TS loss. A detailed ROC investigation of the EADL-BTMIC model on the test dataset is portrayed in Figure 9. The results indicated that the EADL-BTMIC model exhibited its ability to categorize three different classes, such as meningioma, pituitary, and glioma, on the test dataset. Table 4 provides a comprehensive comparison study of the EADL-BTMIC model with other models [15; 17]. Figure 10 demonstrates a brief \(accu_{y}\) and \(spec_{y}\) assessment of the EADL-BTMIC approach with existing models. The figure indicates that the AlexNet-FC7 method has shown lower performance with minimal \(accu_{y}\) and \(spec_{y}\) of 91.21% and 90.36%, respectively. Followed by the Alexnet-FC6, VGG19-CNN, VGG19-GRU, and VGG19-Bi-GRU models, which accomplished moderately closer values of \(accu_{y}\) and \(spec_{y}\). However, the EADL-BTMIC model has gained maximum \(accu_{y}\) and \(spec_{y}\) of 99.06% and 99.30%, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Accuracy** & **Precision** & **Recall** & **Specificity** \\ \hline AlexNet-FC6 & 95.44 & 94.96 & 97.53 & 95.30 \\ AlexNet-FC7 & 91.21 & 90.48 & 95.48 & 90.36 \\ VGG19-CNN & 95.06 & 94.68 & 94.54 & 95.48 \\ VGG19-GRU & 96.61 & 94.12 & 94.69 & 94.91 \\ VGG19-Bi-GRU & 94.14 & 95.77 & 96.10 & 95.14 \\ EADL-BTMIC & 99.06 & 98.34 & 98.61 & 99.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative analysis of EADL-BTMIC approach with recent algorithms. Figure 9: ROC curve analysis of EADL-BTMIC technique. Figure 11 illustrates a detailed \(prec_{n}\) and \(reca_{l}\) analysis of the EADL-BTMIC system with recent methods. The figure shows that the AlexNet-FC7 algorithm has shown lower performance with minimal \(prec_{n}\) and \(reca_{l}\) of 90.48% and 95.48%, correspondingly. Afterward, the Alexnet-FC6, VGG19-CNN, VGG19-GRU, and VGG19-Bi-GRU techniques accomplished moderately closer values of \(prec_{n}\) and \(reca_{l}\). Finally, the EADL-BTMIC technique gained maximal \(prec_{n}\) and \(reca_{l}\) of 98.34% and 98.61%, correspondingly. Figure 10: \(Accu_{y}\) and \(spec_{y}\) analysis of EADL-BTMIC technique with recent algorithms. The above-mentioned results and discussion ensured that the EADL-BTMIC model resulted in enhanced classification outcomes over other methods.