# A Hybrid Learning-Architecture for Improved Brain Tumor Recognition
## 5 Discussion
The latter quantitatively assesses the model's ability to identify a specific class with \"1\" and \"0\", indicating the best and the worst performance, respectively. Notably, in the ROC curves for the first datasets, (Figure 5) the normal and pituitary class reside closest to the top-left corner (AUC is \(\sim\)100%), while the meningioma and glioma class exhibits a smaller distance from this position. A similar observation can be drawn from Figure 6 for the second dataset. The ROC curve and AUC scores show that our architecture correctly classified each class well. For individual model performance in predicting each class, the ROC curves in Figure 4d-f also revealed that the ViT model performed the best in predicting all classes as it has curves that are more tilted to the top left corner, while the CNN ROC curves are the farthest from the top left corner amongst the three individual models. This is consistent with the results in Table 3. The confusion matrix in Figure 4a-c further evaluates the performance of the three models as it shows the proportion of instances each model predicted correctly. The comparative accuracy, in Table 4, contrasting the proposed method against recent literature work tested on various brain tumor datasets, highlight its advantages. Some of the compared methods (e.g., refs. [11; 12; 13; 18; 19; 20; 26]) were tested partially on the public dataset in Table 1, while others (e.g., refs. [21; 29] adopted a method similar to our approach of integrating various datasets to reduce model overfitting and avoid unbalanced data class scenarios. No previous studies have utilized the same three datasets used in our study. However, by employing this strategy, we aim to showcase the distinct contributions and advantages of our proposed model without directly competing with or undermining the efforts of other authors. While this may not represent the optimum comparative scenario, it offers valuable context by illustrating how our framework stands relative to recent work that shares similarities in dataset usage. This approach allows for a nuanced evaluation of our framework's performance and position within the broader research landscape utilizing similar datasets, thus providing valuable insights for readers and researchers. In our proposed framework, we emphasize critical issues concerning the scientific community but also offer a solution with far-reaching implications beyond brain tumor classification alone. While the presented architecture is designed specifically for brain tumor classification, its underlying principles and methodologies have the potential to be used in a variety of oncology applications. The weighted fusion of multiple learnable modules within our ensemble architecture enables the capture of both localized and long-range dependencies. The latter increases our system's ability to identify complex patterns indicative of brain tumors with high sensitivity. The transferability of pre-trained models and feature extraction techniques suggests that they could be applied to other cancer types, opening the door to multi-modal and multi-organ image analysis frameworks. Despite the promising results, our method has some limitations, including the reliance on a single image modality (i.e., MRI) for prediction, the lack of explainability and interpretability of the machine decisions, and the use of a single ML classifier. The above-mentioned limitations stand as a motivation for future improvement.