干旱区生态环境知识资源中心(Arid): BiLSTM-ANN: early diagnosis of Alzheimer's disease using hybrid deep learning algorithms

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DOI	10.1007/s11042-023-17867-5
	BiLSTM-ANN: early diagnosis of Alzheimer's disease using hybrid deep learning algorithms
	Matlani, Princy
通讯作者	Matlani, P
来源期刊	MULTIMEDIA TOOLS AND APPLICATIONS
ISSN	1380-7501
EISSN	1573-7721
出版年	2024
英文摘要	Alzheimer's disease (AD) is a progressive neurodegenerative disease that affects cognition, behavior, and memory, eventually reaching a point where daily activities are impaired. Although there is currently no cure, initiating a well-considered management approach in the early stages can improve quality of life and potentially slow disease progression. Various machine learning (ML) techniques are widely used in clinical research to aid in the detection and tracking of disease states. Magnetic resonance imaging (MRI) is considered one of the most effective tools for diagnosing Alzheimer's disease. However, detecting subtle changes in the AD-affected brain in the early stages presents a significant challenge. The main challenges are the extremely small numbers of trained samples and larger feature descriptions. Hence, in this research, automatic AD can be diagnosed through the adoption of hybrid deep learning (DL) methodologies. For image pre-processing, Improved adaptive wiener filtering (IAWF) is utilized to enhance the acquired images. Then, the features are extracted by an effective hybrid method named Principal Component Analysis, which uses a Normalized Global Image Descriptor (PCA-NGIST) to extract the significant features from images without any image segmentation. Next, the best features are selected using the Improved Wild Horse Optimization algorithm (IWHO). Finally, the disease is diagnosed by hybrid Bi-directional Long Short-Term Memory with Artificial Neural Network (BiLSTM-ANN). The suggested method is implemented on the MATLAB platform. An accuracy of 99.22% is attained for the ADNI dataset and 98.96% for the OASIS dataset, which are comparatively better than the state-of-the-art methods.
英文关键词	Degenerative neurological disorder Alzheimer's disease Adaptive wiener filtering Normalized global image descriptor Wild horse optimization
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001136127800001
WOS关键词	MODEL
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS研究方向	Computer Science ; Engineering
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404944
推荐引用方式 GB/T 7714	Matlani, Princy. BiLSTM-ANN: early diagnosis of Alzheimer's disease using hybrid deep learning algorithms[J],2024.
APA	Matlani, Princy.(2024).BiLSTM-ANN: early diagnosis of Alzheimer's disease using hybrid deep learning algorithms.MULTIMEDIA TOOLS AND APPLICATIONS.
MLA	Matlani, Princy."BiLSTM-ANN: early diagnosis of Alzheimer's disease using hybrid deep learning algorithms".MULTIMEDIA TOOLS AND APPLICATIONS (2024).

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