Classification on ADHD with Deep Learning

Kuang, Deping; He, Lianghua

doi:10.1109/ccbd.2014.42

Cited by 71 publications

(48 citation statements)

References 13 publications

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“…While DBN is extensively used for image recognition and speech processing, only a few studies have applied it to the complex data of differences in brain morphometry related to psychiatric disorders1819. Plis et al 18.…”

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confidence: 99%

“…Their results showed the high potential of exploratory analysis with DBNs for learning the physiological representations and detecting the latent relationships in neuroimaging data acquired from patients with Huntington’s disease and SCZ. Kuang and He19 applied the DBN to feature extraction and classification tasks in fMRI data acquired as part of the ADHD-200 cohort20. The model was found to be useful in discriminating patients with attention deficit/hyperactivity disorder (ADHD) from controls, and it performed somewhat better than the performance described in the results published at the ADHD-200 competition.…”

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confidence: 99%

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Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia

Pinaya

Gadelha

Doyle

et al. 2016

Sci Rep

151

114

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Neuroimaging-based models contribute to increasing our understanding of schizophrenia pathophysiology and can reveal the underlying characteristics of this and other clinical conditions. However, the considerable variability in reported neuroimaging results mirrors the heterogeneity of the disorder. Machine learning methods capable of representing invariant features could circumvent this problem. In this structural MRI study, we trained a deep learning model known as deep belief network (DBN) to extract features from brain morphometry data and investigated its performance in discriminating between healthy controls (N = 83) and patients with schizophrenia (N = 143). We further analysed performance in classifying patients with a first-episode psychosis (N = 32). The DBN highlighted differences between classes, especially in the frontal, temporal, parietal, and insular cortices, and in some subcortical regions, including the corpus callosum, putamen, and cerebellum. The DBN was slightly more accurate as a classifier (accuracy = 73.6%) than the support vector machine (accuracy = 68.1%). Finally, the error rate of the DBN in classifying first-episode patients was 56.3%, indicating that the representations learned from patients with schizophrenia and healthy controls were not suitable to define these patients. Our data suggest that deep learning could improve our understanding of psychiatric disorders such as schizophrenia by improving neuromorphometric analyses.

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confidence: 99%

mentioning

confidence: 99%

Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia

Pinaya

Gadelha

Doyle

et al. 2016

Sci Rep

151

114

View full text Add to dashboard Cite

show abstract

“…Furthermore, using images from MRI, fMRI and other sources, deep learning has been able to help 3D brain construction using autoencoders and deep CNN [90], neural cell classification using CNN [65], brain tissue classifications using DBN [67,68], tumour detection using DNN [65,66] and Alzheimer's diagnosis using DNN [91].…”

Section: Opportunities In Smart Health Applications For Deep Learningmentioning

confidence: 99%

“…With high accuracy achieved in the detection, proper diagnosis of breast cancer becomes possible in radiology. Kuang and He in [67] modified and used DBN for the classification of attention deficit hyperactivity disorder (ADHD) using images from fMRI data. In a similar fashion, Li et al [68] used the RBM for training and processing the dataset generated from MRI and positron emission tomography (PET) scans with aim of accurately diagnosing Alzheimer's disease.…”

Section: Sensory Data Acquisition and Processing Via Wearables And Camentioning

confidence: 99%

Big Sensed Data Meets Deep Learning for Smarter Health Care in Smart Cities

Obinikpo

Kantarcı

2017

JSAN

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Abstract:With the advent of the Internet of Things (IoT) concept and its integration with the smart city sensing, smart connected health systems have appeared as integral components of the smart city services. Hard sensing-based data acquisition through wearables or invasive probes, coupled with soft sensing-based acquisition such as crowd-sensing results in hidden patterns in the aggregated sensor data. Recent research aims to address this challenge through many hidden perceptron layers in the conventional artificial neural networks, namely by deep learning. In this article, we review deep learning techniques that can be applied to sensed data to improve prediction and decision making in smart health services. Furthermore, we present a comparison and taxonomy of these methodologies based on types of sensors and sensed data. We further provide thorough discussions on the open issues and research challenges in each category.

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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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Classification on ADHD with Deep Learning

Cited by 71 publications

References 13 publications

Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia

Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia

Big Sensed Data Meets Deep Learning for Smarter Health Care in Smart Cities

Prospects of deep learning for medical imaging

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