Abstract
Alzheimer's disease (AD) is an irreversible neurodegenerative disease, and, at present, once it has been diagnosed, there is no effective curative treatment. Accurate and early diagnosis of Alzheimer's disease is crucial for improving the condition of patients since effective preventive measures can be taken in advance to delay the onset time of the disease. 18F-Fluorodeoxyglucose positron emission tomography (18F-FDG PET : PET) is an effective biomarker of the symptom of AD and has been used as medical imaging data for diagnosing AD. Mild cognitive impairment (MCI) is regarded as an early symptom of AD, and it has been shown that MCI also has a certain biomedical correlation with PET. In this paper, we explore how to use 3D PET images to realize the effective recognition of MCI and thus achieve the early prediction of AD. This problem is then taken as the classification of three categories of PET images, including MCI, AD, and NC (normal controls). In order to get better classification performance, a novel network model is proposed in the paper based on 3D convolution neural networks (CNN) and support vector machines (SVM) by utilizing both the excellent abilities of CNN in feature extraction and SVM in classification. In order to make full use of the optimal property of SVM in solving binary classification problems, the three-category classification problem is divided into three binary classifications, and each binary classification is being realized with a CNN + SVM network. Then, the outputs of the three CNN + SVM networks are fused into a final three-category classification result. An end-to-end learning algorithm is developed to train the CNN + SVM networks, and a decision fusion algorithm is exploited to realize the fusion of the outputs of three CNN + SVM networks. Experimental results obtained in the work with comparative analyses confirm the effectiveness of the proposed method.
Highlights
Alzheimer’s disease (AD), as a chronic neurodegenerative disease characterized by irreversible loss of neurons and genetically complex disorder, is often found in the elderly people [1]
We proposed a hybrid model integrated with convolution neural networks (CNN) and support vector machine (SVM) networks to predict NC, mild cognitive impairment (MCI), and AD. e structure of the proposed model is shown in Figure 1 that consists of two modules, a feature extraction module based on CNN with 3D kernels (3DCNN), and a SVM-based classification module
Experiments are conducted for the proposed 3DCNN + SVM classification method and for the other state-of-the-art methods, respectively. e methods proposed in the cited literature were originally designed for solving binary classification problems, such as the prediction of AD vs. NC or MCI vs. NC
Summary
Alzheimer’s disease (AD), as a chronic neurodegenerative disease characterized by irreversible loss of neurons and genetically complex disorder, is often found in the elderly people [1]. In computer-aided AD diagnosis, various pattern recognition-based methods have been employed to predict AD and MCI, and these methods can be roughly divided into two steps, feature extraction and classification. Garali et al [7] proposed a novel brain region validity ranking method to separate AD from healthy controls, where SVM and random forest are employed for classification with the features obtained from selected 21 regions. Cabral and Silveira [9] used different ensemble classifiers based on SVM and random forest to extract diverse features on different sets of brain voxels for classification. Lu et al [10] extracted three groups of spatial features from PET images and proposed a semisupervised classification method based on random manifold learning with affinity regularization for AD detection
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