# A LeViT-EfficientNet-Based Feature Fusion Alzheimer's Disease Diagnosis
## 4 Discussions
In addition, the lack of interpretability may cause challenges to healthcare professionals in understanding the results. The proposed AD detection model integrated the SHAP values in order to provide the results with interpretability. With the recommended feature extraction, it generated an exceptional outcome. [PERSON] et al. (2022) [36] employed ResNet-101, DenseNet-121, and VGG-16 models to detect AD. These models achieved an average accuracy of 97.8%. VGG-16 required parameters of 138 M to generate the outcome, leading to high computational cost. It is lessexpensive compared to the proposed AD model. ResNet-101 architecture was complex, resulting in high training time. It required additional computational power due to the residual connections. DenseNet-121 model required a substantial memory during the training phase. The dense connectivity pattern has reduced the ability to find AD patterns compared to the proposed model. [PERSON] et al. (2022) [37] used VGG-10 to classify the AD classes using MRI images. They fine-tuned VGG-19's performance to improve the prediction accuracy. The fixed architecture of the VGG-19 has reduced the model's performance. The vanishing gradient problem has affected the model's learning ability. The depth and complexity enabled the model to produce results with high computational cost. In addition, VGG-19 demanded substantial memory to store the intermediate results. In contrast, the proposed AD detection model has employed mixed-precision training to reduce the computational power. Moreover, the self-attention mechanism has supported the proposed model's remarkable outcome. [PERSON] et al. (2022) [38] used free surfer segmentation to locate AD patterns. They constructed a gradient-boosting classifier for detecting AD statuses. The processing time of free surfer segmentation may vary depending on the hardware specification. The limited spatial resolutions of MRI have reduced the performance of the model. In addition, augmented samples of 3D MRI were complex, limiting the effectiveness of the AD detection model. In contrast, the proposed AD model combined LeViT and EfficientNet B7 to improve prediction accuracy by producing complex AD patterns. [PERSON] et al. (2023) [39] constructed a shallow CNN model to classify the AD types. They employed 2D CNN for multi-class classification. The model comprised seven convolutional layers trained using the weights of the pre-trained model. It required extensive image pre-processing in order to maintain a considerable performance. The lack of generalization has reduced the model's prediction accuracy. The model's performance was low compared to the proposed model. The author encountered challenges in managing and optimizing the feature extraction processes. The high-dimensional and heterogeneous MRI images caused challenges in extracting intricate AD patterns. However, the EfficientNet B7 and LeViT backbones were fine-tuned to overcome the image complexities. The high risk of overfitting due to integrating LeViT and EfficientNet B7 models was reduced using regularization and effective data augmentation techniques. The authors applied the mixed-precision training strategy to minimize the computational costs for the feature extraction. The proposed AD detection model was generalized on two datasets. A rigorous validation and generalization test is essential in order to ensure the proposed model's robustness and reliability across diverse populations. It can improve the model's trustworthiness in a real-time environment. The integration of the proposed model into the clinical workflow may demand substantial validation, standardization, and flexible user interfaces. The variations in MRI images may influence the model's robustness and generalization. Continuous monitoring and updating are essential in order to adapt to technical advancements and clinical guidelines. AD detection is challenging and requires coordination between computer scientists, neuroscientists, radiologists, and medical professionals.