## 3 Results
### Prediction Performance  
Figure 5 shows the accuracy and loss curves in the training and validation stages, where a blue line indicates training loss and a red line for validation loss indicates the three convolutional models we experimented with during this work, which were trained on four class datasets for 25 epochs. Performance metrics are applied to test data by considering normal, very mild, mild, and moderate AD cases. As shown in Figure 6, in terms of precision, recall, and F1 score, when comparing all methods, it is observed that PDL achieved the lowest loss value of 0.02 and performed the best accuracy of 99%. The lowest accuracy is obtained with ResNet50 (96.5%). For further in-depth evaluation of performance, the results are reported in Table 3.  
The prediction of the early stage of a very mild class is intriguing due to the inherent challenges associated with its diagnosis. In contrast, the classification of the late stage, which falls under the moderate category, is rather straightforward since all algorithms consistently yield a 100% accuracy rate in their results. In the early stages of AD, the PDL demonstrates superior performance with a precision of 100%, recall of 99%, and F1 score of 99%. In contrast, the ResNet50 and VGG16 models exhibit lower predictions, as shown in \begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Class Label**} & **Precision** & **Recall** & **F1-Score** & **Average** \\  & & **(\%)** & **(\%)** & **(\%)** & **Score** \\ \hline \multirow{3}{*}{ResNet50} & Normal & 98 & 97 & 98 & 97.6\% \\  & Very Mild & 93 & 97 & 95 & 95\% \\  & Mild & 97 & 91 & 94 & 94\% \\  & Moderate & 100 & 100 & 100 & 100\% \\  & Normal & 98 & 99 & 99 & 98.6\% \\  & Very Mild & 98 & 97 & 97 & 97.3\% \\  & Mild & 100 & 98 & 99 & 99\% \\  & Moderate & 100 & 100 & 100 & 100\% \\  & Normal & 99 & 99 & 98 & 98.6\% \\  & Very Mild & 100 & 99 & 99 & 99.3\% \\  & Moderate & 100 & 100 & 100 & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance measures: comparison between pertained models architecture based on AD patients. Figure 6: (**A**) The performance metric of F1 score, where ResNet50 results are shown in blue, VGG16 results are shown in yellow, and PDL results are shown in green. (**B**) The performance metric of precision, where ResNet50 results are shown in blue, VGG16 results are shown in yellow, and PDL results are shown in green. (**C**) The performance metric of recall, where ResNet50 results are shown in blue, VGG16 results are shown in yellow, and PDL results are shown in green. Table 3. Consequently, it is evident that PDL has superior performance in comparison to the other two models. Additionally, in order to conduct a more comprehensive assessment of the classification models, Figure 7 presents a confusion matrix diagram that serves as a concise representation of the prediction outcomes during the evaluation of classification models on test data. The PDL model demonstrates remarkable performance in detecting both normal and early disease cases. In particular, the PDL yields 2-3% superior results compared to the VGG16 and ResNet50 models. It systematically summarizes the number of correctly or incorrectly predicted images. The vertical axis corresponds to the predicted class (output class), while the horizontal axis represents the true class (target class). Each confusion matrix is visually depicted as a heat map, utilizing color-coding techniques. The presence of darker pixels representing the diagonal elements is observable in all of the confusion matrices that have been displayed. This observation suggests that a substantial quantity of data is accurately classified in its corresponding category. In contrast, bright hues show instances of model misclassifications. The PDL achieved accurate classification for 1044 out of 1047 normal images, 698 out of 703 very mild images, and 269 out of 274 mild images.