## 1 Introduction
### Literature Review  
In recent years, researchers have explored various approaches to diagnose and classify AD. This section provides a brief overview of some significant advancements reported in recent studies from the literature. In one recent study [7], MRI and PET scans were employed to differentiate between AD and Normal Cognition (NC) and prodromal Mild Cognitive Impairment (pMCI), as well as between single-modality MCI (sMCI) and NC. The study introduced a novel approach, initially employing a 3D CNN to extract fundamental features. Subsequently, instead of the conventional fully connected (FC) layer, FSBi-LSTM was used to enhance the spatial precision. A SoftMax classifier was then employed for feature classification. Additionally, the number of filters in the convolution layer was reduced to address overfitting concerns. Another study [8] used a spectral graph Convolutional Neural Network (graph-CNN) to analyze T1-weighted MRI data from the ADNI-2 cohort. The objective was to identify MCI and AD and predict the onset of AD in both ADNI-1 and an Asian cohort. The graph-CNN achieved notable accuracy in distinguishing controls vs. AD patients (85.8%) and Early MCI (EMCI) vs. AD (79.2%) within the ADNI-2 cohort, outperforming other deep learning methods. It accurately predicted the conversion from EMCI to AD (75%) and from Late MCI (LMCI) to AD (92%). Furthermore, the fine-tuned graph-CNN demonstrated promising accuracy in NC vs. AD classification in both cohorts (ADNI-1: 89.4%; Asian cohort: >90%). In another recent investigation [9], researchers developed a three-dimensional CNN aimed at AD detection. The model was trained using 1230 PET scans collected from 988 individuals, including 169 cases of AD, 661 cases of MCI, and 400 NC individuals sourced from the ADNI database. Preprocessing involved stripping and normalizing the raw scans to eliminate non-cereb structures, reducing the computational complexity and processing time. The network achieved a comparable accuracy of 88.76% in NC/AD classification tasks. Moreover, in the research presented in [10], a transfer learning strategy leveraging a pre-trained AlexNet was presented for multiclass Alzheimer's disease classification using MRI brain images from the OASIS database, achieving an accuracy rate of 92.85%. In [11], researchers introduced a multi-modal ensemble deep learning (DL) approach using a stacked CNN-BiLSTM to identify AD progression. This method involved extracting local and longitudinal features from each modality and incorporating background knowledge to extract the local features. Subsequently, all extracted features were fused for regression and classification tasks, resulting in an accuracy of 92.62%. In [12], a novel four-dimensional deep learning algorithm (C3d-LSTM) tailored for AD classification, specifically handling functional MRI (fMRI) data, was introduced. This model efficiently leverages spatial information by integrating multiple 3D CNN models to extract data from each region within a three-dimensional static picture sequence obtained from fMRI scans. Subsequently, the extracted features undergo processing using LSTM techniques to capture instantaneous information within the dataset. The outcomes underscore the effectiveness of the C3d-LSTM model in managing four-dimensional fMRI data and accurately discerning their spatiotemporal attributes for AD diagnosis. The investigation detailed in [13] introduces and assesses various deep learning models and architectures, encompassing both two- and three-dimensional CNNs and Recurrent Neural Networks (RNNs). One approach involves employing a 2D CNN on 3D MRI volumes by dividing each MRI scan into two 2D slices, disregarding interconnections among the slices. Alternatively, a CNN model can be preceded by an RNN, allowing the two-dimensional CNN + RNN model to comprehend connections across sequences of two-dimensional slices obtained from MRIs. Through the utilization of a 3D voxel-based technique coupled with transfer learning, the study achieved a classification accuracy rate of 96.88%.