# A Hybrid Learning-Architecture for Improved Brain Tumor Recognition
## 4 Experimental Results  
In this work, the proposed ensemble architecture is evaluated using the public and locally-acquired datasets described in Section 2.1. For the public dataset, the ground truth labels were provided to the researchers and used to assess the accuracy/sensitivity of the proposed system. All patients were first described for the local dataset based on MRI findings on T2, DWI, and contrast-enhanced T1 images. All patients with malignant tumors had a biopsy. The back-end for the proposed architecture was Tensorflow 2.13.0 and Python 3.8. The CNN as a feature extractor consisted of five layers, each followed by a ReLU activation function and a max-pooling layer to improve computational efficiency. A flattened layer was introduced to reshape the feature maps to the one-dimensional vector, passed into an MLP trained over 50 epochs with a batch size of 125 for classification. MLP employed a sparse categorical cross-entropy loss function and Adam optimizer with a learning rate starting at 0.001 and was reduced by default during the training process for better results. The ViT model was configured to extract a 16 \(\times\) 16 image patch size, with 20 layers, 3072 hidden dimensions, and 12 attention heads. A NumPy expanded dimension parameter was added to represent an additional dimension added to the batch dimension. A predicted image parameter was provided to pass the preprocessed image through the ViT model to extract features. A flattened feature parameter converted the extracted features to a one-dimensional vector. The MLP classifier model consisted of a flattened layer with 86,000 features, three dense layers each, ReLU activation, 0.5-dropout, batch normalization, and Softmax activation for the final layer. The MLP model was compiled and evaluated with the Adam optimizer, a batch size of 125, 30 epochs, sparse categorical cross-entropy loss, and accuracy metrics. All of our experiments and analysis were conducted on a Dell workstation with a 12 th Gen Intel(r) Core(tm) i9-12700, 20 processors, 64.0 GB of memory, 1.5 TB disk capacity and an NVIDIA GeForce RTX 3060 with GPU. The end-to-end execution time for testing the proposed ensemble was 60 \(\pm\) 0.29 s, including feature extraction, fusion, and MLP classification. The model assessment used five-fold cross-validation for performance evaluation. As an unbiased estimator, cross-validation enhances how the deep architecture will transfer to an independent dataset and helps to partially decrease overfitting or selection bias. For cross-validation, we used a ratio of 80% to 20% of the entire dataset for training and testing consecutively. In our experiments, quantitative assessment relied on three metrics: accuracy (Ac), sensitivity (Se), and specificity (Sp) [41]. \[\text{Ac}=\frac{\text{Tp}+\text{Tn}}{\text{Tn}+\text{Tn}+\text{Fn}+\text{Fp}} \tag{4}\]  
\[\text{Sn}=\frac{\text{Tp}}{\text{Tp}+\text{Fn}} \tag{5}\]  
\[\text{Sp}=\frac{\text{Tn}}{\text{Tn}+\text{Fp}} \tag{6}\]  
Here, Tp (Tn) represents the number of true positive (negative), and Fp (Fn) represents the number of false positive (negative) samples. The above metrics summarize the overall accuracy in Table 3. To examine the importance of different MRI-derived features, we employed ablation techniques to understand the contributions of individual ensemble components. Three different ablation scenarios were explored: individual modules, paired modules, and all three modules. For paired modules, we assessed the contribution of (1) higher-order texture and deeper features while excluding the ViT-derived features; (2) deeper and ViT-derived features while leaving out the texture features; (3) ViT-derived and texture features while excluding the CNN model.