## 6 Discussion  
This section provides an extensive comparison between BMRINet and existing deep learning methods, highlighting the superior performance of our proposed SETL_BMRI ensemble model in diagnosing brain tumors using the same dataset. In Table 4, the first column represents a cited work, the second column shows the learning model, and the last column indicates an emphasis on dataset inclusion is crucial for contextualizing and interpreting the results effectively.  
The remarkable performance of stacked SETL_BMRI model can be attributed to its sophisticated integration of two distinct CNN models coupled with a data augmentation technique. Data augmentation artificially increases the training dataset through transformations or modifications which improve the model's generalization capacity, mitigate class imbalance, and lower the risk of overfitting. Data augmentation is essential for healthcare applications because the model must require to handle variations in data and anomalies in image classes.  
The proposed model used transfer learning, which is suitable in those situations when gathering a large amount of data is difficult. Transfer learning is the process of building a model on one activity and utilizing it as the foundation for another task. Transfer learning might be able to reduce the need for a lot of training data because it can use what it has learned from a source task that was trained on a lot of datasets, which stores common features and trends. The model needs fewer data to achieve adequate performance because it doesn't have to learn all of the features from scratch. Transfer learning is especially helpful in medical imaging since by utilizing pretrained models it may drastically lower the amount of training data needed. The SETL_BMRI model gains more discriminative power from  
Figure 12: Test results of brain tumor classification: glioma, meningioma, pituitary, no tumor (left to right).  
Figure 13: Comparison of the performance of the proposed model with pre-trained models.  
the addition of a meta-learner with a dense layer. The batch normalization stabilizes and accelerates training, and dropout maintains the model from becoming overly dependent on any one set of features, which improves the model's capacity for generalization. Further, the effectiveness of transfer learning implementation can be greatly influenced by various factors, including the task complexities, the training data relevance and quality from the source model, the degree of similarity between the source and target tasks, and the minimal volume of training data that is necessary. An example in this study is when a model that was initially trained on a sizable dataset for object recognition can be refined to fit a particular object recognition task using a comparatively smaller dataset, since the essential attributes of images (such as edges, textures, and shapes) remain constant.  
The SETL_BMRI model differs from traditional ensemble approaches in that it incorporates various methodologies and architectures to present a more reliable and accurate system for medical image classification. In conclusion, the discussion proposes to investigate substitute techniques to be considered as a future enhancement such as 3D CNNs, attention processes, or other inventive methods. For example, because 3D CNNs can handle volumetric data and offer a more thorough analysis than 2D CNNs, they can be very major in medical imaging. The most relevant portions of the image can be emphasized by models using attention processes (compatible with CNNs), which have been successfully used in natural language processing and medical imaging. Examining these and other methods may result in tools for diagnosis that are more successful and efficient.