Discrimination of ADHD Based on fMRI Data with Deep Belief Network

Kuang, Deping; Guo, Xiaojiao; An, Xiu; Zhao, Yilu; He, Lianghua

doi:10.1007/978-3-319-09330-7_27

Cited by 55 publications

(47 citation statements)

References 15 publications

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“…In the realm of ADHD, several efforts have been made to use publicly available imaging data and deep learning algorithms for diagnosis. In a study published in 2014, Kuang et al attempted to classify ADHD using a deep belief network, comprised of stacked Boltzmann's machines trained on the public ADHD-200 dataset [71]. Using time-series fMRI data, the deep belief network achieved an accuracy of 35.1%.…”

Section: Functional Connectivity and Classification Of Brain Disordersmentioning

confidence: 99%

“…Using time-series fMRI data, the deep belief network achieved an accuracy of 35.1%. While each of the above classifiers have achieved results that are either on-par or less accurate than clinical diagnoses using fMRI data, methods are expected to improve dramatically as the quantity of labeled data continues to grow [71].…”

Section: Functional Connectivity and Classification Of Brain Disordersmentioning

confidence: 99%

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Deep Learning and Neurology: A Systematic Review

2019

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Deciphering the massive volume of complex electronic data that has been compiled by hospital systems over the past decades has the potential to revolutionize modern medicine, as well as present significant challenges. Deep learning is uniquely suited to address these challenges, and recent advances in techniques and hardware have poised the field of medical machine learning for transformational growth. The clinical neurosciences are particularly well positioned to benefit from these advances given the subtle presentation of symptoms typical of neurologic disease. Here we review the various domains in which deep learning algorithms have already provided impetus for change-areas such as medical image analysis for the improved diagnosis of Alzheimer's disease and the early detection of acute neurologic events; medical image segmentation for quantitative evaluation of neuroanatomy and vasculature; connectome mapping for the diagnosis of Alzheimer's, autism spectrum disorder, and attention deficit hyperactivity disorder; and mining of microscopic electroencephalogram signals and granular genetic signatures. We additionally note important challenges in the integration of deep learning tools in the clinical setting and discuss the barriers to tackling the challenges that currently exist.

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Section: Functional Connectivity and Classification Of Brain Disordersmentioning

confidence: 99%

Section: Functional Connectivity and Classification Of Brain Disordersmentioning

confidence: 99%

Deep Learning and Neurology: A Systematic Review

2019

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“…Kuang et al [20] dealt with the prefrontal cortex, visual cortex and cingulate cortex brain areas and the 4-class classification problem for a reduced number of ADHD patients. The authors used a famous deep-learning approach to build a classifier.…”

Section: A Comparison With Existing Methodsmentioning

confidence: 99%

“…A comparison with other techniques may not be valid. Both examined methods [20] and [21] use a reduced number of samples for training and testing. Experiments with the complete set of samples increase the complexity of the classifiers and reduce accuracy.…”

Section: A Comparison With Existing Methodsmentioning

confidence: 99%

An ADHD Diagnostic Approach Based on Binary-Coded Genetic Algorithm and Extreme Learning Machine

Sachnev¹,

Sundaram²

2016

Journal of Computing Science and Engineering

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An accurate approach for diagnosis of attention deficit hyperactivity disorder (ADHD) is presented in this paper. The presented technique efficiently classifies three subtypes of ADHD (ADHD-C, ADHD-H, ADHD-I) and typically developing control (TDC) by using only structural magnetic resonance imaging (MRI). The research examines structural MRI of the hippocampus from the ADHD-200 database. Each available MRI has been processed by a region-of-interest (ROI) to build a set of features for further analysis. The presented ADHD diagnostic approach unifies feature selection and classification techniques. The feature selection technique based on the proposed binary-coded genetic algorithm searches for an optimal subset of features extracted from the hippocampus. The classification technique uses a chosen optimal subset of features for accurate classification of three subtypes of ADHD and TDC. In this study, the famous Extreme Learning Machine is used as a classification technique. Experimental results clearly indicate that the presented BCGA-ELM (binary-coded genetic algorithm coupled with Extreme Learning Machine) efficiently classifies TDC and three subtypes of ADHD and outperforms existing techniques. Category: Smart and intelligent computing

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“…Supervised learning is commonly used in the following two tasks. In classification, the model associates input data with pre-defined categorical results (i.e., normal vs. diseased) [15][16][17]. The output is a discrete categorical variable in classification.…”

Section: From Traditional Machine Learning To Deep Learningmentioning

confidence: 99%

Prospects of deep learning for medical imaging

2018

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Machine learning techniques are essential components of medical imaging research. Recently, a highly flexible machine learning approach known as deep learning has emerged as a disruptive technology to enhance the performance of existing machine learning techniques and to solve previously intractable problems. Medical imaging has been identified as one of the key research fields where deep learning can contribute significantly. This review article aims to survey deep learning literature in medical imaging and describe its potential for future medical imaging research. First, an overview of how traditional machine learning evolved to deep learning is provided. Second, a survey of the application of deep learning in medical imaging research is given. Third, wellknown software tools for deep learning are reviewed. Finally, conclusions with limitations and future directions of deep learning in medical imaging are provided.

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Discrimination of ADHD Based on fMRI Data with Deep Belief Network

Cited by 55 publications

References 15 publications

Deep Learning and Neurology: A Systematic Review

Deep Learning and Neurology: A Systematic Review

An ADHD Diagnostic Approach Based on Binary-Coded Genetic Algorithm and Extreme Learning Machine

Prospects of deep learning for medical imaging

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