## 2 Related Works  
The field of Alzheimer's disease (AD) classification has indeed made significant advancements with the utilisation of multimodal brain imaging data and deep learning approaches. Deep convolutional neural networks (CNNs) have emerged as powerful tools for AD classification due to their ability to leverage the rich information provided by various imaging modalities. Several research studies have been conducted to improve the classification of AD using deep learning techniques. Some studies focus on manually building feature extraction approaches combined with machine learning algorithms, while others utilise deep learning methods. For example, a hybrid deep learning framework was introduced for the early-stage diagnosis and detection of AD [26], and improved CNNs were proposed for classifying AD using multimodal brain imaging data [27]. [PERSON] et al. [28] presented a multi-modality cascaded convolutional network for the prediction and detection of AD. A systematic study on deep-learning-based medical image analysis for disease diagnosis was conducted by [PERSON] [29]. Additionally, a multi-scale feature fusion network was introduced for classifying illnesses like AD using MRI data [30], and a three-channel phase feature learning model was proposed for jointly learning from MRI and PET images for AD diagnosis [31]. Various deep learning methods, including CNNs, stacked deep polynomial networks, and generative adversarial networks (GANs), have also been explored for the diagnosis of AD. For instance, a tensor-train, high-order pooling and semisupervised learning-based GAN (THS-GAN) was presented by [PERSON] et al. [32], and [PERSON] et al. [33] developed multimodal deep polynomial networks. CNNs can be used as feature extractors and classifiers, or solely as feature extractors. Some researchers use CNN to extract features and then apply traditional machine learning methods for classification [34]. CNN has been employed to extract target-level representations generated from sparse regression for clinical decision making [35]. Furthermore, a combination of 3D CNN with MRI was utilised for AD classification [36]. In specific studies, 2D CNNs have been developed and trained using 2D MRI slices as input. [PERSON] et al. [37] incorporated a pre-trained VGG-16 feature extractor from ImageNet and explored the application of Lasso and PCA in conjunction with CNN to predict the conversion of mild cognitive impairment (MCI) to AD. Similarly, in [38], a landmark-based deep multi-instance learning (LDMIL) classifier was proposed, which can detect anatomical landmarks in brain MRI images and define regions of interest (ROIs) for diagnosis using the ADNI dataset. Other approaches have focused on structural magnetic resonance imaging (sMRI) to improve AD diagnosis. For example, a dual-attention multi-instance deep learning model (DA-MIDL) was developed to extract global and local features and create a classification system [39]. Automatic segmentation methods using the ensembling of neural networks on the hippocampus region have also been employed [40; 41]. Furthermore, a semi-supervised multimodal Laplacian regularized least squares (mLapRLS) method was developed to differentiate MCI subjects from healthy controls (HC) [42], and an architecture capable of combining multimodal neuroimaging information in one setting was proposed to classify AD [43]. Lastly, [PERSON] et al. [44] developed a Monte Carlo ensemble neural network on 2D image slices from multiple database sources and achieved an accuracy of 90% for AD classification. The field of Alzheimer's disease (AD) classification has made significant advancements using multimodal brain imaging data and deep learning approaches, particularly deep convolutional neural networks (CNNs). Deep learning eliminates the need for manual feature extraction and allows for the automatic learning of features. CNNs have proven effective in AD classification by leveraging the rich information provided by various imaging modalities.