# A Hybrid Learning-Architecture for Improved Brain Tumor Recognition
## 5 Discussion  
Precise diagnosis and classification of brain tumors is of utmost importance for early intervention. Clinically, various physical and neurological examinations are utilized for diagnosis, and biopsy remains the most reliable diagnostic method, i.e., the gold standard. However, it is invasive and has potential risks of bleeding, tissue injury, and functional loss. As a result, AI-based research utilizes medical imaging for its crucial role in determining the type and stage of the tumor and developing a treatment plan. Over the last decade, various works of literature have been developed that exploited different image modalities (e.g., MRI, CT, etc.) for the prompt identification of brain tumors; the authors of [48] presented a comprehensive review. MRI is the preferred choice as it is a non-ionizing and non-invasive method. The main objective of this work is to develop a robust architecture for brain tumor identification using MRI. The focus is identifying prominent localized and non-localized information-rich features associated with disease, building upon public and local datasets for system training and validation. Thus, this work contributed to a system capable of classifying brain tumors based on rich-MRI-derived cues. We have introduced a hybrid architecture for multi-classification of brain tumors integrating three learning modules to extract texture (radiomics) and deep hidden features, all combined by a feature weighing scenario to form a rich feature vector for tumor classification. Quantitatively, and as shown in Table 3, the integrative efforts of the employed models demonstrate the superiority of our ensemble approach (accuracy \(\geq\) 99%) for both datasets. The subsequent exploration of ablation techniques, dissecting the ensemble model under various scenarios, reveals that the combined ViT with texture features model outperforms others with an impressive overall accuracy of \(\sim\)95%. Further, Table 3 shows the summary of the evaluation metrics of the ensemble and ablated models. The results/performance of CNN-derived features (CnF) alone showed reduced performance compared with the hand-crafted features (Hcf). This can partly be explained by the shallow nature of neural architecture and the integrative nature of three sets of radiomics (i.e., GLCM, GLRM, and LBP). The ViT-based (ViF) classification alone showed the best results, which is explained by its ability to capture long-range dependencies, making it suitable for recognizing complex patterns. The \"CnF+Hcf\", \"CnF+ViF,\" and \"ViF+Hcf\" ablated models' results show the performance of the ensemble model without the ViT, Hcf, and CNN models, respectively. Table 3 showed that the fused experiments with ViT-derived features increase the performance of the individual branch. This affirms the utility of the ViT-derived feature and the fusion strategy employed, where weights are assigned to each feature branch based on its prediction accuracy. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{4}{c}{**Evaluation Metrics, (\%)**} \\ \hline
**Method** & **Dataset** & **Ac** & **Se** & **Sp** \\ \hline [PERSON] and [PERSON] [11] & [17] & 83.00 & 88.00 & 80.00 \\ [PERSON] et al. [12] & [17] & 95.23 & – & – \\ [PERSON] and [PERSON] [13] & [17] & 91.9 & – & – \\ [PERSON] and [PERSON] [18] & [17] & 97.12 & – & – \\ [PERSON] et al. [19] & [17] & 94.82 & 93.00 & 94.60 \\ [PERSON] et al. [20] & [17] & 94.23 & – & – \\ [PERSON] et al. [21] & [17; 42; 43; 44; 45] & 94.20 & – & – \\ [PERSON] et al. [26] & [17] & 98.85 & – & – \\ [PERSON] et al. [28] & [46] & 97.5 & - & – \\ [PERSON] et al. [29] & [17; 46; 47] & 98.56 & 98.64 & 98.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the classification accuracy with state-of-the-art models. In addition to quantitative metrics, robustness analysis is also highlighted using ROC curves, as shown by the interconnected lines in Figures 5 and 6. Generally, the assessment of model effectiveness using those curves is based on the area under the curve (AUC).