## 4 Discussion  
This study tested transfer learning to train our proposed deep-learning algorithm with the objective of accurately classifying different stages of Alzheimer's disease. This study compared our proposed method with two other well-known methods (VGG16 and ResNet50) on Kaggle AD datasets. The findings of our study exhibited a higher level of accuracy and yielded favorable results in comparison to other investigations [45]. The results of our work indicate that pre-trained models achieved high levels of accuracy without requiring data augmentation or extended training epochs. The utilization of hyper-parameters, as informed by previous research [46], involved the adjustment of batch size and learning rate to enhance the learning process and improve generalization accuracy. This enabled us to train CNNs that exhibit effective picture classification capabilities even when using less precise hyper-parameter values. Based on our results, it has been shown that a trained model has the ability to effectively classify AD into distinct phases with a high degree of accuracy. The classification results produced by the three distinct models are presented in Table 3, along with the corresponding values for four performance indicators. The proposed model demonstrated higher performance than VGG16 and Resnet50 for normal cases in terms of overall precision, with scores of 99.00%, 98.00%, and 98%, respectively. The evaluation of F1 score, precision, and recall performance metrics indicates that the proposed framework outperforms VGG16 in most scenarios. Equally, the ResNet50 model exhibits comparatively inferior performance outcomes compared to the other models. The consistency of the AUC values across all categories indicates that the predictions made by the proposed model are stable. Furthermore, the findings indicate that the prediction accuracy of both the VGG16 and ResNet50 models was comparatively lower for the mild and very mild stages. The results also indicated that the utilization of AD improved the ability to classify across all categories. A notable observation is that a significant proportion of the receiver operating characteristic (ROC) curves are situated above the linear reference line that connects the points (0,0) and (1,1). Nevertheless, the  
Figure 8: Representing the ROC curves, the vertical axis represents the true positive rate, and the horizontal axis represents the false positive rate. It shows the class-wise AUC scores obtained by the three models: (**A**) represents the proposed method; (**B**) represents VGG16; and (**C**) represents ResNet50. Blue represents the normal case (class 1), orange represents very mild AD (class 2), green represents mild AD (class 3), and red represents moderate AD (class 4).  
curves in question do not demonstrate a significant closeness to the upper-left corner, mostly because a restricted dataset was used for testing. Based on the confusion matrices depicted in Figure 7, it is apparent that the two highest-performing models exhibited nearly identical levels of accuracy when identifying MRI pictures associated with Alzheimer's disease. The proposed model demonstrates accurate classification, with a success average rate of 98.6% for normal brain pictures and 99.3% for mild and very mild AD images. The VGG16-based model has an accuracy rate of 98.6% for correctly classifying normal cases, 99% for mild AD, and 97.3% for very mild AD images. The ResNet50 model demonstrates an accuracy rate of 97.3% for properly classifying normal brain images, 95% for accurately identifying very mild AD, and 94% for correctly classifying mild AD images. Based on the results of the analysis, it can be concluded that the performance of the proposed model surpassed that of the VGG16 and ResNet50 models for mild and very mild AD cases.