## 1 Introduction
A 16-layer VGGNet was used by the authors of reference [15] to effectively divide structural MRI scans into three groups: Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal cognitive (NC). The authors reported that segmentation was not conducted on the magnetic resonance (MR) images. Using functional MRI, ref. [16; 17] applied the LeNet architecture to classify patients with Alzheimer's disease from healthy controls. They came up with a technique for structural MRI that uses CNNs. The research demonstrated that CNN outperformed SVM. Future studies will likely include axial and sagittal MRI scans in addition to the standard coronal ones. The current result is 98.84% accurate, which is quite good. The use of structural MR images allowed for the development of a CNN-based AD diagnosis model [18]. Researchers found that by using both data augmentation and transfer learning together, overfitting could be lessened, and the models could use less computing power. Previous studies relied on smaller, regional datasets, but the authors of this one claim their work can be applied much more widely. In [19], an 8-layer CNN model was created specifically for AD diagnosis. To find the best model setup, the authors looked at many different activation function combinations, such as stochastic, max, and average pooling with ReLU, sigmoid, and leaky ReLU. A leaky ReLU activation function and a max pooling function were used in the most effective CNN models. A 3D-CNN model trained on MR images was proposed for AD diagnosis in [20]. They propose a 3D-CNN using the ResNet framework. Convolutional, dropout, pooling, and fully connected layers are some of the 36 it contains. The model outperformed expectations on a variety of performance metrics in experimental testing. Another method in [21] using 3D-CNN to examine MR images for AD signs was proposed. The authors deployed a Sobolev gradient optimizer, a leaky ReLU activation function, and a Max Pooling function. The three functions worked better together than separately. To aid in the diagnosis of AD, ref. [22] developed a 3D-FCNN-based model using MR images. The authors revealed that their proposed model outperformed several industry standards for both accuracy and robustness. The 3D-FCNN model outperformed the 2D-CNN on binary and multi-class classification tasks. An approach to diagnosing AD that uses structural MRI, genetic testing, and clinical evaluation was suggested in [23]. It is based on convolutional neural networks. The framework required fewer parameters than rivals like VGGNet and AlexNet for building CNN models. The method was quicker and less prone to overfitting in scenarios with sparse data. In [2], the authors propose using a CNN-based MR image model for the early detection of AD. The OASIS dataset, which is notoriously skewed, was used to train the model. To address the discrepancies present in the OASIS data set, data augmentation was implemented. According to the results of the experiments, the proposed model is superior to several state-of-the-art models. A CNN-based MR image AD diagnosis model was proposed in [24]. It all started with voxelizing MR scans. Skull stripping was used to get rid of extra voxels, and the quality of the remaining ones was enhanced with a Gaussian filter. Independent component analysis was used to separate out different regions of the brain. In the end, the gray matter in the model was segmented. According to the results of the experiments, the proposed model is superior to other state-of-the-art models. The eight-layer CNN AD diagnosis model in [25] used drop-out regularization, data augmentation, and batch normalization to ensure excellent precision. The Siamese Convolutional Neural Network (SCNN) was introduced in [26] as a CNN-based model for dividing dementia into four distinct stages: moderate Alzheimer's disease (MAD), mild dementia (MD), very mild dementia (VMD), and no dementia. Despite just having a small sample size to train on, the model's results were reliable.