DeepAD: Alzheimer’s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI

Sarraf, Saman; Dd, DeSouza; Anderson, John A. E.; Tofighi, Ghassem

doi:10.1101/070441

Cited by 206 publications

(196 citation statements)

References 31 publications

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“…Ils favorisent le dépistage de certains cancers, de fractures et même d'atteintes dues à la maladie d'Alzheimer [15]. En évaluant différentes caractéristiques cliniques et globales, ces outils peuvent aider au diagnostic et à la prédiction/prévision de certaines pathologies comme des cancers [7,16].…”

Section: Des Outils Précieux Pour Les Praticiensunclassified

L’intelligence artificielle à l’aide des myologues

Beckmann¹,

Peyrou

Gallay

et al. 2017

Med Sci (Paris)

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Section: Des Outils Précieux Pour Les Praticiensunclassified

L’intelligence artificielle à l’aide des myologues

Beckmann¹,

Peyrou

Gallay

et al. 2017

Med Sci (Paris)

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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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“…The starting and ending slices do not contain brain tissues. Therefore, we only extracted middle cross-section for AD detection [7]. The patch size we selected was 56 56 5 × × , which means we merged five neighboured slice as one patch.…”

Section: S H Luo Et Almentioning

confidence: 99%

“…It achieved high accuracy of 87%. [7] outlined deep learning-based pipelines employed to distinguish Alzheimer's MRI and fMRI from normal healthy control data for a given age group. It almost perfectly distinguished Alzheimer's patients from healthy normal brains.…”

Section: Introductionmentioning

confidence: 99%

Automatic Alzheimer’s Disease Recognition from MRI Data Using Deep Learning Method

Luo¹,

Li²,

Li³

2017

JAMP

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Alzheimer's Disease (AD), the most common form of dementia, is an incurable neurological condition that results in a progressive mental deterioration. Although definitive diagnosis of AD is difficult, in practice, AD diagnosis is largely based on clinical history and neuropsychological data including magnetic resource imaging (MRI). Increasing research has been reported on applying machine learning to AD recognition in recent years. This paper presents our latest contribution to the advance. It describes an automatic AD recognition algorithm that is based on deep learning on 3D brain MRI. The algorithm uses a convolutional neural network (CNN) to fulfil AD recognition. It is unique in that the three dimensional topology of brain is considered as a whole in AD recognition, resulting in an accurate recognition. The CNN used in this study consists of three consecutive groups of processing layers, two fully connected layers and a classification layer. In the structure, every one of the three groups is made up of three layers, including a convolutional layer, a pooling layer and a normalization layer. The algorithm was trained and tested using the MRI data from Alzheimer's Disease Neuroimaging Initiative. The data used include the MRI scanning of about 47 AD patients and 34 normal controls. The experiment had shown that the proposed algorithm delivered a high AD recognition accuracy with a sensitivity of 1and a specificity of 0.93.

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DeepAD: Alzheimer’s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI

Cited by 206 publications

References 31 publications

L’intelligence artificielle à l’aide des myologues

L’intelligence artificielle à l’aide des myologues

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

Automatic Alzheimer’s Disease Recognition from MRI Data Using Deep Learning Method

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