## 2 Materials and Methods
### Proposed Method for AD Diagnosis
It has been demonstrated that transfer learning is a dependable and effective initial method for developing interpretable deep-learning models. Transfer learning is an approach that applies pre-trained networks to novel tasks by modifying them, thereby efficiently classifying diverse datasets. Classifying medical images, such as brain MRI scans, with models initially trained on natural images from ImageNet [42] proves to be a particularly advantageous application. During training, this study utilizes pre-trained weights obtained from ImageNet [42]. Then, retraining networks on a new dataset through the modification of the final fully connected layers, except for the final fully connected layer, pre-trained layers are frozen, and each model is retrained using the dataset in this scenario. In order to generate class prediction probabilities, the output layer incorporates a fully connected layer with Softmax activation and a global pooling layer (Global Average Pooling). The selection of ResNet50 and VGG16 for our research was predicated on their distinct attributes and benefits within the domain of medical image classification, specifically in the context of transfer-learning-based Alzheimer's disease (AD) diagnosis. ResNet50 and VGG16 are pre-trained models, as described in [42]. By utilizing deep residual learning, ResNet50 overcomes the difficulty associated with training extremely deep networks. Additionally, ResNet50's skip connection lets each layer make a link between its input and output, which makes it easier for the model to understand complicated features in medical images and speeds up the flow of data. The VGG16 architecture is selected due to its simple design, which incorporates deeper networks utilizing smaller convolutional filters (3 \(\times\) 3). The simplicity of this approach facilitates the comprehension of acquired features, which is particularly critical in medical situations where it is vital to grasp the model's reasoning process. Moreover, every layer in VGG16 represents a distinct level of abstraction, thereby establishing a distinct hierarchy of features. The hierarchical structure of this representation proves to be highly advantageous in the field of medical image analysis, wherein the significance of multiple levels of detail varies. The configuration parameters for the three tested models are summarized in Table 1. Under identical conditions, the objective of this configuration is to compare the F1 score,  
Figure 4: CNN architecture. recall, and precision of these various neural network architectures. This guarantees that the hyperparameters for all three models are identical. Throughout the experimental procedure, data preprocessing was conducted in a consistent manner for all models, thereby guaranteeing consistent data partitions for the purposes of training, validation, and testing as follows:  
* For a consistent starting point, identical weights were assigned to each model during initialization. * During training, the designated hyper-parameters were applied to the training set, while the progress of training was consistently monitored and assessed on the validation set. * Using identical test set, performance metrics were computed for every model undergoing evaluation. * To determine whether or not there were significant differences in performance metrics between models, statistical tests were employed, including ROC curve tests. The results were graphically represented, incorporating precise recall curves or confusion matrices, which offered valuable insights into the merits and demerits of every model. Proposed deep learning (PDL) is a CNN framework using EfficientNetB0, where architecture is presented in Table 2, and a few other compounds explained in the next section. EfficientNetB0 has been purposefully engineered to attain competitive performance while minimizing computational demands.