Nonlinear feature transformation and deep fusion for Alzheimer's Disease staging analysis

Shi, Bibo; Chen, Yani; Zhang, Pin; Smith, Charles D.; Liu, Jundong

doi:10.1016/j.patcog.2016.09.032

Cited by 78 publications

(39 citation statements)

References 34 publications

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“…Table 4 shows some studies in Alzheimer's disease and other forms of dementia via machine learning algorithms. The applications include diagnosis of Alzheimer's disease [115,116], diagnosis of dementias [117], and detection of Alzheimer's disease related regions [118], prediction of mild cognitive impairment patients for conversion to Alzheimer's disease [119,120], detection of dissociable multivariate morphological patterns [121], diagnosis of both Alzheimer's disease and mild cognitive impairment [122] and identification of genes related to Alzheimer's disease [125,126]. Alzheimer's disease: sensitivity = 85%, specificity = 82%, accuracy = 85%; Mild cognitive impairment: sensitivity = 84%, specificity = 81%, accuracy = 85% [125] Identification of genes related to Alzheimer's disease DT; QAR 33 90 genes are related to Alzheimer's disease [126] Identification of genes related to Alzheimer's disease ELM; RF; SVM 31 Sensitivity= 78.77%; Specificity= 83.1%; Accuracy = 74.67% DCNN = deep convolutional neural network; DT = decision tree; ELM = extreme learning machine; EM = expectation maximization; GA = genetic algorithm; LC = lasso classification; LDS = low density separation; LR = logistic regression; NBC = Naive Bayes classifier; QAR = quantitative association rules; RF = random forest; RLO = random linear oracle; RS = random subspace; SVM = support vector machine.…”

Section: Alzheimer's Disease and Other Forms Of Dementiamentioning

confidence: 99%

“…Fourteen different algorithms were employed in [115][116][117][118][119][120][121][122][123][124][125][126]. The datasets of Alzheimer's disease and other forms of dementia have relatively small sample size.…”

Section: Alzheimer's Disease and Other Forms Of Dementiamentioning

confidence: 99%

“…The datasets of Alzheimer's disease and other forms of dementia have relatively small sample size. Three applications, prediction of mild cognitive impairment patients for conversion to Alzheimer's disease [119,120], prediction of mild cognitive impairment [122][123][124] and identification of genes related to Alzheimer's disease [125,126] are required to have further improvement3.4. Tuberculosis Tuberculosis (TB) was a lethal infectious disease caused by bacteria mycobacterium tuberculosis that usually attacks the lung.…”

Section: Alzheimer's Disease and Other Forms Of Dementiamentioning

confidence: 99%

“…Due to the multitude of smart healthcare applications, only four applications in the field of diseases diagnosis, cardiovascular diseases [82][83][84][85][86][87][88][89][90][91][92][93][94][95][96][97][98][99], diabetes mellitus [100][101][102][103][104][105][106][107][108][109][110][111][112], Alzheimer's disease and other forms of dementias [113][114][115][116][117][118][119][120][121][122][123][124][125][126], and tuberculosis [127][128][129][130][13...…”

Section: Introductionmentioning

confidence: 99%

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Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

et al. 2017

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Abstract:To promote sustainable development, the smart city implies a global vision that merges artificial intelligence, big data, decision making, information and communication technology (ICT), and the internet-of-things (IoT). The ageing issue is an aspect that researchers, companies and government should devote efforts in developing smart healthcare innovative technology and applications. In this paper, the topic of disease diagnosis in smart healthcare is reviewed. Typical emerging optimization algorithms and machine learning algorithms are summarized. Evolutionary optimization, stochastic optimization and combinatorial optimization are covered. Owning to the fact that there are plenty of applications in healthcare, four applications in the field of diseases diagnosis (which also list in the top 10 causes of global death in 2015), namely cardiovascular diseases, diabetes mellitus, Alzheimer's disease and other forms of dementia, and tuberculosis, are considered. In addition, challenges in the deployment of disease diagnosis in healthcare have been discussed.

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Section: Alzheimer's Disease and Other Forms Of Dementiamentioning

confidence: 99%

Section: Alzheimer's Disease and Other Forms Of Dementiamentioning

confidence: 99%

Section: Alzheimer's Disease and Other Forms Of Dementiamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

et al. 2017

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“…In this endeavor machine-learning, especially deep-learning algorithms, have the potential to show exceptional promise [6][7][8][9]. To this end, we have been successful in developing a machine learning algorithm that allow us to classify fMRI ADHD scans from normal healthy brain scans without using any demographic information.…”

Section: Current and Future Research Directionsmentioning

confidence: 99%

Towards quantifying psychiatric diagnosis using machine learning algorithms and big fMRI data

Saeed

2018

Big Data Anal

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Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common brain disorders among children and is very difficult to diagnose using current methods. Similarly other mental disorders are subject to the same systematic errors with sufficient evidence of diagnostic errors as well as over-prescribing of drugs due to misdiagnosis . For most mental health disorders there is no quantitative method that will inform the presence or absence of a given mental disorder. We argue that definitive and quantitative diagnostic tests are necessary for ADHD and other mental disorders. To this end, big data Functional Magnetic Resonance Imaging (fMRI) and machine learning algorithms can be instrumental in changing the way psychiatric disorders are diagnosed and treated. We briefly discuss our recent research efforts and future directions for a quantitative gold standard tests for psychiatric diagnosis.Keywords: Machine learning and data mining, ADHD, fMRI, Big data, High performance computing Current and future research directionsIt is widely known that defining and diagnosing mental disorders is a difficult process due to overlapping nature of symptoms, and lack of a biological test that can serve as a definite and quantified gold standard [1]. To this end, Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common brain disorders among children and relies on the identification of abnormal mental characteristics. ADHD is notoriously difficult to diagnose, especially in children, with sufficient evidence of diagnostic errors as well as over-prescribing of drugs due to misdiagnosis [2]. The current psychiatric diagnosis is based purely on behavioural observation (DSM-5/ICD-10) but lacks biological and/or genetic validity and is prone to errors [1]. For most mental health disorders definitive and quantitative diagnostic tests which can detect the presence or the absence of a specific psychiatric disorder(s) are non-existent [1,3].Functional Magnetic Resonance Imaging (fMRI) is a non-invasive technique for studying the brain functional activities and is based on Blood Oxygen Level Dependent (BOLD) contrast [4]. During the fMRI scan, a series of images are taken using a scanner while the subject to a specific task such as resting, or doing various pre-determined tasks. The Saeed Big Data Analytics (2018) 3:7 Page 2 of 3 result of brain scanning is a series of low resolution images over time which shows the activity of the brain and can allow us to develop a highly informative brain connectome. There is evidence that ADHD, Bipolar Disorder (BD) and Schizophrenia have characteristics that differ in the regional and global connectivity of the brain when studied under resting state Functional Magnetic Resonance Imaging or fMRI [1,3,5].As data-scientists, along with our colleagues from psychiatry and neuroscience, the overarching question that we have been posing is as follows: Can we diagnose a person with ADHD (or other mental disorders) using fMRI scans using techniques from machine learning and novel ...

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An efficient multiclass classifier for classification of Alzheimer's disease/mild cognitive impairment/Normal subjects

Dora

Agrawal

Panda

et al. 2021

Int J Imaging Syst Tech

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Typically, in sparse representation‐based classifiers, the weight associated with each training sample is ignored, resulting in reduced accuracy. Moreover, individual binary classifiers solved a multiclass problem. It requires more time as multiple runs are needed to compute the accuracy. In this paper, we propose a novel optimal sparse representation‐based classifier. It solves the ternary classification problem with improved accuracy in a single run. The ternary classification considers Alzheimer's disease versus mild cognitive impairment versus normal control in a single run. A two‐stage sparse representation model is used to design the proposed classifier. To update the weight coefficients, we suggest a regularized Levenberg–Marquardt learning. It allows selecting a subset of significant training samples. To determine the appropriate subset size, we investigate an objective function in terms of classification accuracy. For optimization, we suggest a hybrid particle swarm optimization–squirrel search technique. The experiment conducted on the Alzheimer's Disease Neuroimaging Initiative database shows our method outperforms other state‐of‐the‐art methods in terms of computation time and accuracy. The use of different training–testing partition ratios makes the proposed method immune to biased results, overfitting, and underfitting difficulties. Moreover, results are obtained from 100 iterations to confirm its stability. The suggested model may be helpful for further research in medical image analysis.

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Nonlinear feature transformation and deep fusion for Alzheimer's Disease staging analysis

Cited by 78 publications

References 34 publications

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

Towards quantifying psychiatric diagnosis using machine learning algorithms and big fMRI data

An efficient multiclass classifier for classification of Alzheimer's disease/mild cognitive impairment/Normal subjects

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