前驱期
人工智能
计算机科学
正电子发射断层摄影术
神经影像学
图像融合
磁共振成像
认知障碍
模式识别(心理学)
融合
深度学习
传感器融合
认知
机器学习
医学
放射科
精神科
图像(数学)
哲学
语言学
作者
Shubham Dwivedi,Tripti Goel,M. Tanveer,R. Murugan,Rahul Sharma
出处
期刊:IEEE MultiMedia
[Institute of Electrical and Electronics Engineers]
日期:2022-03-07
卷期号:29 (2): 45-55
被引量:27
标识
DOI:10.1109/mmul.2022.3156471
摘要
Alzheimer’s disease (AD) is a prevalent, irreversible, chronic, and degenerative disorder whose diagnosis at the prodromal stage is critical. Mostly, single modality data, such as magnetic resonance imaging (MRI) or positron emission tomography (PET), are used to make predictions in AD studies. However, the metabolic and structural data fusion can provide a holistic view of AD-staging analysis. To achieve this objective, a novel multimodal fusion-based method is proposed in this article. An optimal fusion of MRI and PET is achieved by harnessing demon algorithm and discrete wavelet transform. Finally, the fused image features are extracted using ResNet-50, and these features are classified using robust energy least square twin support vector machine classifier. Experiments on the AD neuroimaging initiative dataset show descent accuracy of 97%, 94%, and 97.5% for cognitive normal (CN) versus AD, CN versus mild cognitive impairment (MCI), and AD versus MCI, respectively. The proposed model will be beneficial for health professionals in accurately diagnosing AD at an early stage.
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