# A LeViT-EfficientNet-Based Feature Fusion Alzheimer's Disease Diagnosis
## 4 Discussions  
In this study, the author introduced an EfficientNet B7 and LeViT-based feature fusion technique for extracting key features from MRI images. The EfficientNet B7 model was improved by integrating the attention mechanism. In addition, the author trained the EfficientNet B7 model using mixed-precision training to reduce the computational cost. A fine-tuned DXB model was used to detect AD using the extracted features. The model was trained and tested using the OASIS Alzheimer's dataset. A data augmentation technique was employed in order to provide adequate training to the model to learn intricate patterns of AD. The author generalized the model using the Alzheimer's dataset. Table 3 highlights the performance of the proposed AD detection model. The model produced an outstanding performance by achieving an accuracy of 98.9% and specificity of 98.7%. Tables 4 and 5 reveal the findings of the comparative analysis using the existing backbones. Table 6 outlines the findings of the existing AD detection models. The proposed model has outperformed the existing AD detection models. It required less computational power to identify AD. The recommended feature fusion technique has supported the proposed model in delivering an optimal outcome. In addition, the suggested BOHB optimization has fine-tuned the parameters of the DXB model to make an effective decision with limited resources. The proposed model demonstrated remarkable performance with limited computational costs. Models with exceptional AUROC and AUPRC can assist healthcare professionals in diagnostic interpretation and treatment options. The proposed AD detection model can empower clinicians to make effective decisions and offer personalized care to individuals. It holds promise for improving patient outcomes and advancing the understanding of AD symptoms in the earlier stages. By integrating computational approaches with clinical practice, this study enhanced AD detection using MRI images. The proposed model's accuracy and efficiency have significant clinical implications. Effective AD detection enables physicians to diagnose, schedule, and track disease development. Reliable diagnostic techniques and timely intervention can enhance patient outcomes and quality of life. Moreover, scientific communities may benefit from the study findings to extend the research in medical imaging analysis and DL methods. The author trained the proposed model using the OASIS dataset that covers the MRI images with biomarkers, including an individual's age, sex, cognitive score, and diagnosis status. Researchers can gain insights into the underlying AD pathology and build effective diagnostic and therapeutic strategies. The proposed model allows researchers to identify critical biomarkers, including brain atrophy, cortical thickness changes, hippocampus alterations, white matter integrity alterations, and abnormalities in specific brain regions. Integrating SHAP values facilitates healthcare professionals to identify the significance of MRI biomarkers (features) associated with AD. The proposed model assigns a positive and negative SHAP value to each feature. Healthcare professionals can use SHAP values to understand the importance of features in AD prediction. For instance, a SHAP value of 0.7 related to brain atrophy feature indicates that higher activation in the brain atrophy region is associated with AD prediction. In contrast, a negative SHAP value is associated with a decreased likelihood of AD. [PERSON] and [PERSON] (2021) [35] employed AlexNet, Visual Geometry Group (VGG)-16, and ResNet-50 to extract features from MRI images. They used a Support Vector Machine to predict AD. The pre-trained CNN models may produce biased predictions, leading to false positives. The limited generalization ability has reduced the model's performance in the context of AD prediction. The class imbalances have reduced the Support Vector Machine model's capability of detecting AD.