干旱区生态环境知识资源中心(Arid): A LeViT-EfficientNet-Based Feature Fusion Technique for Alzheimer's Disease Diagnosis

Arid

DOI	10.3390/app14093879
	A LeViT-EfficientNet-Based Feature Fusion Technique for Alzheimer's Disease Diagnosis
	Sait, Abdul Rahaman Wahab
通讯作者	Sait, ARW
来源期刊	APPLIED SCIENCES-BASEL
EISSN	2076-3417
出版年	2024
卷号	14 期号:9
英文摘要	Alzheimer's disease (AD) is a progressive neurodegenerative condition. It causes cognitive impairment and memory loss in individuals. Healthcare professionals face challenges in detecting AD in its initial stages. In this study, the author proposed a novel integrated approach, combining LeViT, EfficientNet B7, and Dartbooster XGBoost (DXB) models to detect AD using magnetic resonance imaging (MRI). The proposed model leverages the strength of improved LeViT and EfficientNet B7 models in extracting high-level features capturing complex patterns associated with AD. A feature fusion technique was employed to select crucial features. The author fine-tuned the DXB using the Bayesian optimization hyperband (BOHB) algorithm to predict AD using the extracted features. Two public datasets were used in this study. The proposed model was trained using the Open Access Series of Imaging Studies (OASIS) Alzheimer's dataset containing 86,390 MRI images. The Alzheimer's dataset was used to evaluate the generalization capability of the proposed model. The proposed model obtained an average generalization accuracy of 99.8% with limited computational power. The findings highlighted the exceptional performance of the proposed model in predicting the multiple types of AD. The recommended integrated feature extraction approach has supported the proposed model to outperform the state-of-the-art AD detection models. The proposed model can assist healthcare professionals in offering customized treatment for individuals with AD. The effectiveness of the proposed model can be improved by generalizing it to diverse datasets.
英文关键词	feature extraction deep learning transformer LeViT hyperparameter tuning model optimization neuroimaging neurodegenerative diseases
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001219869500001
WOS关键词	DEEP LEARNING ALGORITHMS
WOS类目	Chemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS研究方向	Chemistry ; Engineering ; Materials Science ; Physics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/402874
推荐引用方式 GB/T 7714	Sait, Abdul Rahaman Wahab. A LeViT-EfficientNet-Based Feature Fusion Technique for Alzheimer's Disease Diagnosis[J],2024,14(9).
APA	Sait, Abdul Rahaman Wahab.(2024).A LeViT-EfficientNet-Based Feature Fusion Technique for Alzheimer's Disease Diagnosis.APPLIED SCIENCES-BASEL,14(9).
MLA	Sait, Abdul Rahaman Wahab."A LeViT-EfficientNet-Based Feature Fusion Technique for Alzheimer's Disease Diagnosis".APPLIED SCIENCES-BASEL 14.9(2024).

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