干旱区生态环境知识资源中心(Arid): 3-Way hybrid analysis using clinical and magnetic resonance imaging for early diagnosis of Alzheimer's disease

Arid

DOI	10.1016/j.brainres.2024.149021
	3-Way hybrid analysis using clinical and magnetic resonance imaging for early diagnosis of Alzheimer's disease
	Farhatullah; Chen, Xin; Zeng, Deze; Mehmood, Atif; Khan, Rizwan; Shahid, Farah; Ibrahim, Mostafa M.
通讯作者	Chen, X
来源期刊	BRAIN RESEARCH
ISSN	0006-8993
EISSN	1872-6240
出版年	2024
卷号	1840
英文摘要	Alzheimer's is a progressive neurodegenerative disorder that leads to cognitive impairment and ultimately death. To select the most effective treatment options, it is crucial to diagnose and classify the disease early, as current treatments can only delay its progression. However, previous research on Alzheimer's disease (AD) has had limitations, such as inaccuracies and reliance on a small, unbalanced binary dataset. In this study, we aimed to evaluate the early stages of AD using three multiclass datasets: OASIS, EEG, and ADNI MRI. The research consisted of three phases: pre-processing, feature extraction, and classification using hybrid learning techniques. For the OASIS and ADNI MRI datasets, we computed the mean RGB value and used an averaging filter to enhance the images. We balanced and augmented the dataset to increase its size. In the case of the EEG dataset, we applied a band-pass filter for digital filtering to reduce noise and also balanced the dataset using random oversampling. To extract and classify features, we utilized a hybrid technique consisting of four algorithms: AlexNet-MLP, AlexNetETC, AlexNet-AdaBoost, and AlexNet-NB. The results showed that the AlexNet-ETC hybrid algorithm achieved the highest accuracy rate of 95.32% for the OASIS dataset. In the case of the EEG dataset, the AlexNet-MLP hybrid algorithm outperformed other approaches with the highest accuracy of 97.71%. For the ADNI MRI dataset, the AlexNet-MLP hybrid algorithm achieved an accuracy rate of 92.59%. Comparing these results with the current state of the art demonstrates the effectiveness of our findings.
英文关键词	Alzheimer 's Early diagnosis EEG MRI OASIS
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001249246700001
WOS关键词	OPEN ACCESS SERIES ; MRI DATA
WOS类目	Neurosciences
WOS研究方向	Neurosciences & Neurology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403072
推荐引用方式 GB/T 7714	Farhatullah,Chen, Xin,Zeng, Deze,et al. 3-Way hybrid analysis using clinical and magnetic resonance imaging for early diagnosis of Alzheimer's disease[J],2024,1840.
APA	Farhatullah.,Chen, Xin.,Zeng, Deze.,Mehmood, Atif.,Khan, Rizwan.,...&Ibrahim, Mostafa M..(2024).3-Way hybrid analysis using clinical and magnetic resonance imaging for early diagnosis of Alzheimer's disease.BRAIN RESEARCH,1840.
MLA	Farhatullah,et al."3-Way hybrid analysis using clinical and magnetic resonance imaging for early diagnosis of Alzheimer's disease".BRAIN RESEARCH 1840(2024).