Abstract
Alzheimer’s disease (AD), the most common type of dementia, is a progressive disease beginning with mild memory loss, possibly leading to loss of the ability to carry on a conversation and respond to environments. It can seriously affect a person’s ability to carry out daily activities. Therefore, early diagnosis of AD is conducive to better treatment and avoiding further deterioration of the disease. Magnetic resonance imaging (MRI) has become the main tool for humans to study brain tissues. It can clearly reflect the internal structure of a brain and plays an important role in the diagnosis of Alzheimer’s disease. MRI data is widely used for disease diagnosis. In this paper, based on MRI data, a method combining a 3D convolutional neural network and ensemble learning is proposed to improve the diagnosis accuracy. Then, a data denoising module is proposed to reduce boundary noise. The experimental results on ADNI dataset demonstrate that the model proposed in this paper improves the training speed of the neural network and achieves 95.2% accuracy in AD vs. NC (normal control) task and 77.8% accuracy in sMCI (stable mild cognitive impairment) vs. pMCI (progressive mild cognitive impairment) task in the diagnosis of Alzheimer’s disease.
Highlights
Alzheimer’s disease (AD) is a progressive neurodegenerative disease with insidious onset
To process the noise regions to increase training speed while improving diagnostic accuracy, we propose a data denoising method that reduces the noises at the boundaries of the 3D Magnetic resonance imaging (MRI) images through the designed clipping algorithm
In order to see the effect of the data denoising module on the AD diagnosis, we visually show the features extracted from the last fully connected layer in the AD vs. normal controls (NC) and stable mild cognitive impairment (sMCI) vs. progressive mild cognitive impairment (pMCI) diagnosis tasks
Summary
Alzheimer’s disease (AD) is a progressive neurodegenerative disease with insidious onset. It is characterized by general dementia such as memory impairment, aphasia, impairment of visuospatial skills, and executive dysfunction. A global Alzheimer’s disease report from the International Association for Alzheimer’s disease pointed out that the number of AD patients in the world has increased dramatically. It may increase from 47 million in 2015 to 130 million in 2050, and the cost of AD treatment will increase sharply, from 800 billion in 2015 to 2 trillion in 2030. The elderly population with Alzheimer’s disease will increase. Computer-aided diagnosis (CAD) of AD can help to detect the disease as early as possible and inhibit its progression, which has important significance in research and practical applications
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