Recent Progresses in Neural Networks for Alzheimer's Disease Detection DOI Open Access
Mengyao Zhao

Published: Oct. 12, 2023

This article reviews the introduction of Alzheimer's Disease (AD), neural networks, training and learning applications networks in early diagnosis AD, AD drug discovery, other brain diseases, challenges faced by AD. First, paper introduces background characteristics is a degenerative neurological disorder characterized impaired memory, decreased cognitive function, loss neurons. These place huge burden on lives families patients. Next, basic principle structure network are discussed. A computational model made up multiple neurons that can perform tasks adapting to input data. In particular, key concepts hierarchy, activation function weight adjustment Then, methods Common techniques such as backpropagation algorithm gradient descent optimizer introduced detail, well importance data preprocessing evaluation. focuses application By extracting features from image data, automatically identify differences between patients healthy subjects, enabling intervention. addition, discovery also analyzing predicting database known drugs, help discover potential treatments for speed process. The further explores diseases highlights lack reliable biomarkers, complex pathological mechanisms, etc. summary, this presents systematic overview associated with

Language: Английский

Recent Progresses in Neural Networks for Alzheimer's Disease Detection DOI Open Access
Mengyao Zhao

Published: Oct. 12, 2023

This article reviews the introduction of Alzheimer's Disease (AD), neural networks, training and learning applications networks in early diagnosis AD, AD drug discovery, other brain diseases, challenges faced by AD. First, paper introduces background characteristics is a degenerative neurological disorder characterized impaired memory, decreased cognitive function, loss neurons. These place huge burden on lives families patients. Next, basic principle structure network are discussed. A computational model made up multiple neurons that can perform tasks adapting to input data. In particular, key concepts hierarchy, activation function weight adjustment Then, methods Common techniques such as backpropagation algorithm gradient descent optimizer introduced detail, well importance data preprocessing evaluation. focuses application By extracting features from image data, automatically identify differences between patients healthy subjects, enabling intervention. addition, discovery also analyzing predicting database known drugs, help discover potential treatments for speed process. The further explores diseases highlights lack reliable biomarkers, complex pathological mechanisms, etc. summary, this presents systematic overview associated with

Language: Английский

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