Denouements of machine learning and multimodal diagnostic classification of Alzheimer’s disease

Visual Computing for Industry, Biomedicine, and Art

Manan Shah¹  Binny Naik²  Ashir Mehta² 

[1] Department of Chemical Engineering, School of Technology, Pandit Deendayal Petroleum University, 382007, Gandhinagar, Gujarat, India;Department of Computer Engineering, Indus University, 382115, Ahmedabad, Gujarat, India;

关键词: Machine learning;    Support vector machine;    Alzheimer;   

DOI  :  10.1186/s42492-020-00062-w

来源: Springer

【 摘 要 】

Alzheimer’s disease (AD) is the most common type of dementia. The exact cause and treatment of the disease are still unknown. Different neuroimaging modalities, such as magnetic resonance imaging (MRI), positron emission tomography, and single-photon emission computed tomography, have played a significant role in the study of AD. However, the effective diagnosis of AD, as well as mild cognitive impairment (MCI), has recently drawn large attention. Various technological advancements, such as robots, global positioning system technology, sensors, and machine learning (ML) algorithms, have helped improve the diagnostic process of AD. This study aimed to determine the influence of implementing different ML classifiers in MRI and analyze the use of support vector machines with various multimodal scans for classifying patients with AD/MCI and healthy controls. Conclusions have been drawn in terms of employing different classifier techniques and presenting the optimal multimodal paradigm for the classification of AD.

【 授权许可】

CC BY