Improving MRI-based diagnosis of Alzheimer's disease via an ensemble privileged information learning algorithm

2017 ◽  
Author(s):  
Xiao Zheng ◽  
Jun Shi ◽  
Qi Zhang ◽  
Shihui Ying ◽  
Yan Li
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Learning Algorithm ◽  
Privileged Information

scholarly journals Automatic Diagnosis of Alzheimer’s Disease and Mild Cognitive Impairment Based on CNN + SVM Networks with End-to-End Training

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhe Huang ◽  
Minglang Sun ◽  
Chengan Guo
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Learning Algorithm ◽  
Binary Classification ◽  
Onset Time ◽  
Decision Fusion ◽  
Support Vector ◽  
End To End

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.

scholarly journals Diagnosis of Alzheimer’s Disease Severity with fMRI Images Using Robust Multitask Feature Extraction Method and Convolutional Neural Network (CNN)

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Morteza Amini ◽  
MirMohsen Pedram ◽  
AliReza Moradi ◽  
Mahshad Ouchani
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Early Stage ◽  
Support Vector ◽  
Feature Extraction Method

The automatic diagnosis of Alzheimer’s disease plays an important role in human health, especially in its early stage. Because it is a neurodegenerative condition, Alzheimer’s disease seems to have a long incubation period. Therefore, it is essential to analyze Alzheimer’s symptoms at different stages. In this paper, the classification is done with several methods of machine learning consisting of K -nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), linear discrimination analysis (LDA), and random forest (RF). Moreover, novel convolutional neural network (CNN) architecture is presented to diagnose Alzheimer’s severity. The relationship between Alzheimer’s patients’ functional magnetic resonance imaging (fMRI) images and their scores on the MMSE is investigated to achieve the aim. The feature extraction is performed based on the robust multitask feature learning algorithm. The severity is also calculated based on the Mini-Mental State Examination score, including low, mild, moderate, and severe categories. Results show that the accuracy of the KNN, SVM, DT, LDA, RF, and presented CNN method is 77.5%, 85.8%, 91.7%, 79.5%, 85.1%, and 96.7%, respectively. Moreover, for the presented CNN architecture, the sensitivity of low, mild, moderate, and severe status of Alzheimer patients is 98.1%, 95.2%,89.0%, and 87.5%, respectively. Based on the findings, the presented CNN architecture classifier outperforms other methods and can diagnose the severity and stages of Alzheimer’s disease with maximum accuracy.

scholarly journals A simple classification framework for predicting Alzheimer’s disease from region-based grey matter volume and APOE genotype status

2020 ◽  
Vol 8 (2) ◽  
pp. 15
Author(s):  
Reyhaneh Ghoreishiamiri ◽  
Graham Little ◽  
Matthew R. G. Brown ◽  
Russell Greiner
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Grey Matter ◽  
Learning Algorithm ◽  
Apoe Genotype ◽  
Structural Mri ◽  
Grey Matter Volume ◽  
Healthy Controls ◽  
Diagnostic Systems ◽  
Initial Dataset

Alzheimer’s Disease (AD) is a prevalent neurodegenerative disease currently affecting more than 47 million people in the world. There are now many complex classifiers that can accurately distinguish AD patients from healthy controls, based on the subject’s structural magnetic resonance imaging (MRI) brain scan. Most such automated diagnostic systems are blackboxes: While their predictions are accurate, it is difficult for clinicians to interpret those predictions, due to the large number of features used by the classifier, and/or by the complexity of that classifier. This work demonstrates that an automated learning algorithm can produce a simple classifier that can correctly distinguish AD patients from healthy controls (HC) similar to its more-complex counterparts. Here we buildthis classifier from the data in the Alzheimer’s Disease Neuroimaging Initiative database, using a fairly small set of features, including grey matter volumes of 33 regions of interest derived from structural MRI, as well as the APOE genotype. We first considered three simple base-learners that each produce a classifier that is simple and interpretable. Running our overall learner, involving standard feature selection processes and these simple base-learners, on these features, produced a 7-feature elastic net model, EN7, that achieved accuracy of 89.28% on the test set. Next, we ran the same overall learner using two more-complex base-learners over the same initial dataset. The accuracy of the best model here was 90.47%, which was not statistically different from the performance of our much simpler EN7 model.

scholarly journals Predicting cognitive decline and conversion to Alzheimer’s disease in older adults using the NAB List Learning test

2010 ◽  
Vol 16 (4) ◽  
pp. 651-660 ◽  
Author(s):  
BRANDON E. GAVETT ◽  
AL OZONOFF ◽  
VLADA DOKTOR ◽  
JOSEPH PALMISANO ◽  
ANIL K. NAIR ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Older Adults ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Longitudinal Research ◽  
Learning Algorithm ◽  
Rate Of Change ◽  
List Learning ◽  
Learning Test ◽  
Consensus Diagnosis

AbstractTo validate the Neuropsychological Assessment Battery (NAB) List Learning test as a predictor of future multi-domain cognitive decline and conversion to Alzheimer’s disease (AD), participants from a longitudinal research registry at a national AD Center were, at baseline, assigned to one of three groups (control, mild cognitive impairment [MCI], or AD), based solely on a diagnostic algorithm for the NAB List Learning test (Gavett et al., 2009), and followed for 1–3 years. Rate of change on common neuropsychological tests and time to convert to a consensus diagnosis of AD were evaluated to test the hypothesis that these outcomes would differ between groups (AD>MCI>control). Hypotheses were tested using linear regression models (n = 251) and Cox proportional hazards models (n = 265). The AD group declined significantly more rapidly than controls on Mini-Mental Status Examination (MMSE), animal fluency, and Digit Symbol; and more rapidly than the MCI group on MMSE and Hooper Visual Organization Test. The MCI group declined more rapidly than controls on animal fluency and CERAD Trial 3. The MCI and AD groups had significantly shorter time to conversion to a consensus diagnosis of AD than controls. The predictive validity of the NAB List Learning algorithm makes it a clinically useful tool for the assessment of older adults. (JINS, 2010, 16, 651–660.)

scholarly journals Alzheimer's Disease Classification Using Deep CNN

2021 ◽  
pp. 325-331
Author(s):  
Shikha Agrawal ◽  
Neha Sunil Pandharkar ◽  
Pooja Arvind Khandelwal ◽  
Pratiksha Ashok Pandhare ◽  
Janhavi Sanjay Deoghare
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Learning Algorithm ◽  
Disease Classification ◽  
Data Sets ◽  
Cognitive Tests ◽  
Deep Learning Algorithm ◽  
The World ◽  
Deep Cnn ◽  
Work Done

Especially in the world, the deep learning algorithm has become a technique of choice for analyzing medical images rapidly. Alzheimer's disease (AD) is regarded to be the most prevalent cause of dementia, and only 1 in 4 individuals with Alzheimer's are estimated to be diagnosed correctly on time. However, there is no refractory available treatment, the disorders can be managed when the loss is still mild and the treatment is most effective when it is initiated before significant downstream damage, i.e. mild cognitive impairment (MCI) or earlier steps. Physiological, neurological analysis, neurological and cognitive tests are clinically diagnosed with AD. A better diagnostic needs to be developed, which is addressed in this paper. We concentrate on Alzheimer's disease in this article and discuss different methods are available to detect Alzheimer's. Reviewed the different data sets available for studying data on Alzheimer's disease and finally comparing appropriate work done in this area.

scholarly journals Identifying and ranking potential driver genes of Alzheimer’s disease using multiview evidence aggregation

2019 ◽  
Vol 35 (14) ◽  
pp. i568-i576 ◽  
Author(s):  
Sumit Mukherjee ◽  
Thanneer M Perumal ◽  
Kenneth Daily ◽  
Solveig K Sieberts ◽  
Larsson Omberg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Genome Wide Association Study ◽  
Learning Algorithm ◽  
Late Onset ◽  
External Validation ◽  
Driver Genes ◽  
Feature Sets ◽  
Ranking Scheme

Abstract Motivation Late onset Alzheimer’s disease is currently a disease with no known effective treatment options. To better understand disease, new multi-omic data-sets have recently been generated with the goal of identifying molecular causes of disease. However, most analytic studies using these datasets focus on uni-modal analysis of the data. Here, we propose a data driven approach to integrate multiple data types and analytic outcomes to aggregate evidences to support the hypothesis that a gene is a genetic driver of the disease. The main algorithmic contributions of our article are: (i) a general machine learning framework to learn the key characteristics of a few known driver genes from multiple feature sets and identifying other potential driver genes which have similar feature representations, and (ii) A flexible ranking scheme with the ability to integrate external validation in the form of Genome Wide Association Study summary statistics. While we currently focus on demonstrating the effectiveness of the approach using different analytic outcomes from RNA-Seq studies, this method is easily generalizable to other data modalities and analysis types. Results We demonstrate the utility of our machine learning algorithm on two benchmark multiview datasets by significantly outperforming the baseline approaches in predicting missing labels. We then use the algorithm to predict and rank potential drivers of Alzheimer’s. We show that our ranked genes show a significant enrichment for single nucleotide polymorphisms associated with Alzheimer’s and are enriched in pathways that have been previously associated with the disease. Availability and implementation Source code and link to all feature sets is available at https://github.com/Sage-Bionetworks/EvidenceAggregatedDriverRanking.

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