## 4 Performance Validation  
In this study, the performance validation of the EADL-BTMIC model is carried out using the Figshare dataset [24]. The details related to the dataset are shown in Table 1. A few sample images are illustrated in Figure 3. The dataset holds 3064 T1-weighted contrast-enhanced images with 3 kinds of BT. The dataset includes 708 images in the Meningioma class, 930 images in the Pituitary class, and 1426 images in the Glioma class. In this study, the experimental validation occurs in two distinct ways by splitting the dataset into two aspects based  
Figure 4 demonstrates the confusion matrices formed by the EADL-BTMIC model on test data. With 80% of TR data, the EADL-BTMIC model identified 553, 7144, and 1121 samples into MEN, PIT, and GLI classes, respectively. In addition, with 20% of TS data, the EADL-BTMIC methodology identified 127, 189, and 288 samples into MEN, PIT, and GLI classes, correspondingly. Additionally, with 70% of TR data, the EADL-BTMIC system identified 493, 644, and 977 samples into MEN, PIT, and GLI classes, correspondingly. Finally, with 30% of TS data, the EADL-BTMIC technique identified 210, 268, and 429 samples into MEN, PIT, and GLI classes, respectively. Table 2 and Figure 5 depict the overall classifier results of the EADL-BTMIC model on 80% of training (TR) data and 20% of testing (TS) data. The experimental values exposed that the EADL-BTMIC technique exhibited effectual outcomes. For instance, with 80% of TR data, the EADL-BTMIC model obtained increased \(accu_{y}\), \(prec_{n}\), \(rec_{l}\), \(spec_{y}\), and \(F_{score}\) of  
Figure 4: Confusion matrices of EADL-BTMIC technique (**a**) 80% of TR data, (**b**) 20% of TS data, (**c**) 70% of TR data, and (**d**) 30% of TS data. 98.29%, 97.37%, 97.04%, 98.64%, and 97.20%, respectively. At the same time, with 20% of TS data, the EADL-BTMIC methodology reached enhanced \(accu_{y}\), \(prec_{n}\), \(rec_{l}\), \(spec_{y}\), and \(F_{score}\) of 99.02%, 98.22%, 98.54%, 99.27%, and 98.37%, correspondingly. Table 3 and Figure 6 display the overall classifier results of the EADL-BTMIC technique on 70% of TR data and 30% of TS data. The experimental values revealed that the EADL-BTMIC technique illustrated effectual outcomes. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Labels** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F-Score** \\ \hline
**Training Phase (80\%)** & & & & & \\ Meningioma & 98.25 & 97.19 & 95.34 & 99.14 & 96.26 \\ Pituitary & 98.29 & 97.28 & 97.01 & 98.83 & 97.14 \\ Glioma & 98.33 & 97.65 & 98.77 & 97.95 & 98.20 \\
**Average** & **98.29** & **97.37** & **97.04** & **98.64** & **97.20** \\
**Testing Phase (20\%)** & & & & & \\ Meningioma & 99.02 & 96.21 & 99.22 & 98.97 & 97.69 \\ Pituitary & 99.02 & 99.47 & 97.42 & 99.76 & 98.44 \\ Glioma & 99.02 & 98.97 & 98.97 & 99.07 & 98.97 \\
**Average** & **99.02** & **98.22** & **98.54** & **99.27** & **98.37** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Result analysis of EADL-BTMIC technique with various measures on 80% of TR and 20% of TS data. Figure 5: Result analysis of EADL-BTMIC technique on 80% of TR and 20% of TS data. For sample, with 70% of TR data, the EADL-BTMIC technique attained enhanced \(accu_{y}\), \(prec_{n}\), \(rec_{n}\), \(rec_{l}\), \(spec_{y}\), and \(F_{score}\) of 99.07%, 98.45%, 98.67%, 99.29%, and 98.55%, correspondingly. Simultaneously, with 30% of TS data, the EADL-BTMIC approach reached increased \(accu_{y}\), \(prec_{n}\), \(rec_{l}\), \(spec_{y}\), and \(F_{score}\) of 99.06%, 98.34%, 98.61%, 99.30%, and 98.46%, correspondingly. Figure 7 offers the accuracy and loss graph analysis of the EADL-BTMIC approach on a distinct set of TR/TS datasets. The outcomes demonstrated that the accuracy value is enhanced, and the loss value tends to reduce with a higher epoch count. It is also observed that the training loss is minimal, and validation accuracy is superior on distinct sets of TR/TS datasets.