# A LeViT-EfficientNet-Based Feature Fusion Alzheimer's Disease Diagnosis
## 3 Results  
The performance evaluation of the proposed model was conducted using Windows 11 Pro, Intel i9-12900k, 16 GB RAM, NVIDIA RTX 4090, and Python 3.8.0. The libraries, including Pytorch 1.9, TensorFlow 2.11.0, Theano 1.0.5, and Keras 2.12.0, were used for model development. The OASIS Alzheimer's dataset was divided into a train set (70%), a validation set (15%), and a test set (15%). Alzheimer's dataset (20%) was used to generalize the proposed AD detection model. Table 2 reveals the experimental settings for implementing the proposed AD detection model. The performance of the proposed AD detection during the training and validation phase is highlighted in Figure 4a,b. Compared to the training phase, there was a significant improvement in the validation phase. The recommended early-stopping strategies and regularization techniques have improved the model performance by monitoring the validation loss. The model has attained an optimal performance at the 77 th epoch. The findings of the performance validation using dataset 1 are outlined in Table 3. The recommended LeViT-EfficientNet B7 feature extraction has improved the prediction accuracy of the proposed model. In addition, the data augmentation has supported the model in identifying the critical patterns associated with AD. Figure 5 presents the findings of a comparative analysis of the existing transformer and CNN backbones. The proposed model has outperformed the existing models by obtaining an optimal generalization accuracy of 99.8%. The recommended fine-tuning processes assisted the proposed model in addressing the overfitting, vanishing gradient, and amplification effects. Figure 6 highlights the computational loss of the AD detection models. The proposed model produced a minimal loss compared to the existing models. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameters** & **Values** \\ \hline \multirow{8}{*}{LeViT} & Image Size & 224 \(\times\) 224 \(\times\) 3 \\ \cline{2-3}  & Decay Factor & 0.1 every 10 epochs \\ \cline{2-3}  & Initial Learning Rate & 0.001 \\ \cline{2-3}  & Batches & 43 \\ \cline{2-3}  & Epochs & 75 \\ \cline{2-3}  & Loss Function & Cross-Entropy \\ \cline{2-3}  & Optimizer & Adam \\ \cline{2-3}  & Fusion Layer & Element-wise addition \\ \hline \multirow{8}{*}{EfficientNet B7} & Image & 224 \(\times\) 224 \(\times\) 3 \\ \cline{2-3}  & Optimizer & Adam \\ \cline{2-3}  & Loss Function & Cross-Entropy \\ \cline{2-3}  & Validation Loss Monitor & Early Stopping \\ \cline{2-3}  & Regularization & Dropout, L1, and L2 \\ \cline{2-3}  & Convolutional Layers & 5 \\ \cline{2-3}  & Activation Function & Softmax \\ \hline \multirow{8}{*}{DXB} & Learning Rate & (\(\eta\), [0, 1]) \\ \cline{2-3}  & Minimum Split Loss & (\(\gamma\), [0, \(\infty\)]) \\ \cline{2-3}  & Maximum Tree Depth & ([0, \(\infty\)]) \\ \cline{2-3}  & Optimizer & BOHB \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental Settings. Figure 4: (a) Prediction Accuracy and (b) Loss. Table 4 presented that the proposed model required a few parameters and FLOPs to deliver a remarkable outcome compared to the existing backbones. The findings indicated that the model can be implemented in a resource-constrained healthcare environment. The BOHB algorithm has supported the proposed model in maintaining a trade-off between high generalization accuracy and limited computational resources. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Classes** & **Accuracy** & **Specificity** & **Kappa** & **Precision** & **Recall** & **F1-Score** \\ \hline Mild & 99.8 & 99.9 & 97.5 & 99.3 & 99.5 & 99.4 \\ \hline Moderate & 99.9 & 99.8 & 96.8 & 98.6 & 99.4 & 99.0 \\ \hline Normal & 99.6 & 100 & 97.3 & 99.3 & 99.5 & 99.4 \\ \hline Very mild & 99.8 & 99.8 & 97.9 & 99.5 & 99.6 & 99.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Outcomes of Performance Validation. Figure 5: The Comparative Analysis Outcomes. Figure 6: Computational Loss. Table 5 highlights the findings of the reliability and consistency analysis.