# A Novel Deep Dense Block-Based Model for Detecting Alzheimer's Disease
## 4 Results and Experiments
### Experimental Results  
In this study, as mentioned in Section 3.3, TL was employed to train all DL models. We tested four deep TL models and evaluated their performances based on the indicators described in Section 3.4. The purpose of this research was to evaluate the effectiveness of the proposed deep TL model for the detection of AD and its early stages and to compare its performance with the most advanced CNN models reported in the existing literature. In order to achieve the main goals of this study, we carried out a comparative analysis of each model. To achieve this, we examined the test dataset's average F1 score, sensitivity, specificity, and accuracy scores for all models. According to the results presented in Table 6, the Xception + PPC model demonstrated the highest overall performance on the dataset, with an accuracy of 95.81%. Moreover, this model exhibited the highest sensitivity, specificity, precision, and F1 score, with values of 95.41%, 97.92%, 95.01%, and 95.21%, respectively. The Xception + PPC model also demonstrated excellent sensitivity, which is crucial for minimizing the misdiagnosis rate of volumetric changes in brain tissue. These findings indicate that Xception and PPC can effectively differentiate between Alzheimer's stages. The Xception architecture uses a technique called separable convolution. This technique is a more efficient computational method than traditional convolution. Depthwise separable convolutions offer superior expressiveness and productivity compared with classical convolutions. By incorporating depth-wise separable convolutions, the Xception model becomes highly proficient at learning distinct and high-level features that may be overlooked by simpler models. Another successful model is the ResNet-50V2 + PPC model, which achieves an accuracy of 93.35%. Additionally, the sensitivity, specificity, and F1 scores reached 92.81%, 96.59%, and 92.57%, respectively. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{\
\begin{tabular}{c} **Training** \\ **Accuracy** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Validation** \\ **Accuracy** \\ \end{tabular} } & \multicolumn{6}{c}{**Test (Macro Avg)**} \\ \cline{4-9}  & & & **Accuracy** & **Specificity** & **Precision** & **Sensitivity** & **F1-Score** \\ \hline VGG-16 + PPC & 94.98\% & 90.55\% & 89.66\% & 94.94 & 87.94 & 89.04 & 88.14 \\ VGG-19 + PPC & 95.63\% & 93.39\% & 91.63\% & 95.89 & 90.07 & 90.89 & 90.32 \\ ResNet-50V2 + PPC & 97.54\% & 94.01\% & 93.35\% & 96.59 & 92.41 & 92.82 & 92.57 \\ Xception + PPC & 97.60\% & 95.89\% & 95.81\% & 97.92 & 95.01 & 95.41 & 95.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The performance evaluation and comparison of deep TL architectures using various indicators for the purpose of Alzheimer’s diagnosis. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Classes**} & \multirow{2}{*}{**Original Data**} & \multicolumn{3}{c}{**Augmentation Techniques**} \\ \cline{3-5}  & & **Rotation** & **Flipping** & **Zoom** \\ \cline{3-5}  & & **45\({}^{\circ}\)** & **Horizontal** & **(20\%)** \\ \hline AD & 308 & 308 & 308 & 308 \\ CN & 614 & 614 & 614 & 614 \\ MCI & 1014 & 1014 & 1014 & 1014 \\ Total & 1936 & & 5808 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Data augmentation parameters. Figure 5 shows the accuracy achieved in the test datasets for all models. The test accuracy depicted in Figure 5 is computed by dividing the number of correctly classified patients (CN, MCI, and AD) by the total number of patients. Figure 5 clearly shows that the Xception + PPC model outperformed the other three models. Table 7 presents the performance of the models by class. The best results are bolded. For training purposes, 1936 medical images were utilized, while 406 additional images were designated for testing. The analysis included three classes: AD, MCI, and CN. From the results table, it is evident that the Xception + PPC model exhibits strong performance across all classes.