# A LeViT-EfficientNet-Based Feature Fusion Alzheimer's Disease Diagnosis
## 1 Introduction
The brain structural changes, including hippocampal shrinkage and other AD-related changes in addition to malignancies and strokes, can be identified using MRI [22]. These changes can be used to determine brain abnormalities associated with mild cognitive impairment, which may indicate AD. PET imaging can identify AD's beta-amyloid plaques and tau protein tangles in the brain. PET scans utilizing florbetapir, flutemetamol, or florbetapen may confirm AD [23]. [PERSON] et al. (2023) [24] discussed the significance of integrating neuropsychological assessment with neuroimaging in order to identify AD in its initial stages. Researchers can obtain valuable information on brain anatomical components from high-resolution MRI images. In addition, MRI images have been made available through public open access databases. These datasets are frequently updated, and researchers can utilize them to develop automated AD detection. DL models can improve early detection, understand disease pathology, integrate image features, leverage large-scale datasets, and advance personalized medicine for individuals with AD [25]. These models can capture complex and high-dimensional patterns in medical imaging data, including MRI and PET, assisting in diagnosing and understanding AD [26]. By identifying biomarkers and subtypes of AD, DL-based models may enable individualized treatments [26]. DL algorithms can learn complex representations from massive data, providing improved precision and generalizable AD detection models [27; 28; 29; 30; 31]. [PERSON] and [PERSON] (2017) [32] employed a multi-class classification model to detect AD. [PERSON] et al. (2020) [33] introduced a binary classification to distinguish individuals with and without AD using MRI data. [PERSON] et al. (2021) [34] proposed a DL model to predict AD and dementia. [PERSON] and [PERSON] (2021) [35] used a Support Vector Machine and Deep Neural Network to detect AD using MRI. [PERSON] et al. (2022) [36] used a DL model for AD detection. [PERSON] et al. (2022) [37] proposed a DL model to predict AD in the early stages. [PERSON] et al. (2022) [38] employed a three-dimensional deep convolutional neural network to predict AD in the early stages. [PERSON] et al. (2022) [39] employed a multi-class classification model to detect AD in the early stages. [PERSON] et al. network (CNN) to differentiate individuals with mild AD from those without AD. [PERSON] et al. (2023) [39] pre-processed the MRI scans and improved the CNN model's capability in identifying AD. However, the existing AD detection models demand high-performance graphical or tensor processing units and large-scale computing infrastructure for training and inference. An effective fine-tuning algorithm is required to find the optimal hyperparameters for optimal outcomes. Hyperparameter selection requires substantial testing and manual adjustment, which is time-consuming and computationally expensive. Overfitting or poor generalization may result from inadequate data for deep learning models. Furthermore, researchers and practitioners with restricted computing resources may encounter challenges in model implementation. Existing AD detection approaches using MRI require human interpretation or essential feature extraction, limiting diagnosis accuracy and reliability. There is a demand for advanced and automated techniques to detect subtle disease-specific AD patterns. Transformer-based architectures and CNNs have produced promising results in medical image processing. The integration of transformers and CNNs can extract local and global spatial information from complex images. Combining these architectures may strengthen AD detection feature extraction frameworks. These features have motivated the author to build a hybrid transformer and CNN-based AD detection model. The contributions of the study are as follows:  
A feature fusion-driven LeViT-EfficientNet B7-based feature extraction model to extract the crucial features of AD.