Automated Detection of Autism Spectrum Disorder Using a Convolutional Neural Network

Sherkatghanad, Zeinab; Akhondzadeh, Mohammadsadegh; Salari, Soorena; Zomorodi‐Moghadam, Mariam; Abdar, Moloud; Acharya, U. Rajendra; Khosrowabadi, Reza; Salari, Vahid

doi:10.3389/fnins.2019.01325

Cited by 175 publications

(130 citation statements)

References 40 publications

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“…Finally, iv) the images were normalized onto the standard Montreal Neurological Institute (MNI) space (4 mm) with the non-linear registration algorithm from Ants (15). All the above steps were configured using C-PAC's singularity image 4 .…”

Section: Resting-state Functional Mri Preprocessingmentioning

confidence: 99%

“…In this approach, about one million voxels in space are locally averaged in about 100 to 400 different brain regions depending on the atlas, thus considerably reducing the dimension of the input data. These 100 to 400 averaged time courses are then either used in a 1-D convolutional network (3) or are first used to calculate a crosscorrelation matrix which subsequently is used as a feature in various machine learning methods (4,5). For example, (4) reached an accuracy of 70% on ABIDE-I using the cross-correlation matrix on time courses extracted using the CC400 atlas.…”

Section: Introductionmentioning

confidence: 99%

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Classifying Autism Spectrum Disorder Using the Temporal Statistics of Resting-State Functional MRI Data With 3D Convolutional Neural Networks

et al. 2020

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Resting-state functional magnetic resonance imaging (rs-fMRI) data are 4-dimensional volumes (3-space + 1-time) that have been posited to reflect the underlying mechanisms of information exchange between brain regions, thus making it an attractive modality to develop diagnostic biomarkers of brain dysfunction. The enormous success of deep learning in computer vision has sparked recent interest in applying deep learning in neuroimaging. But the dimensionality of rs-fMRI data is too high (~20 M), making it difficult to meaningfully process the data in its raw form for deep learning experiments. It is currently not clear how the data should be engineered to optimally extract the time information, and whether combining different representations of time could provide better results. In this paper, we explored various transformations that retain the full spatial resolution by summarizing the temporal dimension of the rs-fMRI data, therefore making it possible to train a full three-dimensional convolutional neural network (3D-CNN) even on a moderately sized [~2,000 from Autism Brain Imaging Data Exchange (ABIDE)-I and II] data set. These transformations summarize the activity in each voxel of the rs-fMRI or that of the voxel and its neighbors to a single number. For each brain volume, we calculated regional homogeneity, the amplitude of low-frequency fluctuations, the fractional amplitude of lowfrequency fluctuations, degree centrality, eigenvector centrality, local functional connectivity density, entropy, voxel-mirrored homotopic connectivity, and autocorrelation lag. We trained the 3D-CNN on a publically available autism dataset to classify the rs-fMRI images as being from individuals with autism spectrum disorder (ASD) or from healthy controls (CON) at an individual level. We attained results competitive on this task for a combined ABIDE-I and II datasets of~66%. When all summary measures were combined the result was still only as good as that of the best single measure which was regional homogeneity (ReHo). In addition, we also applied the support vector machine (SVM) algorithm on the same dataset and achieved comparable results,

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Section: Resting-state Functional Mri Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classifying Autism Spectrum Disorder Using the Temporal Statistics of Resting-State Functional MRI Data With 3D Convolutional Neural Networks

et al. 2020

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“…For AAL, 67.5% is the highest accuracy obtained (using augmented data), whereas TT achieved an accuracy of 65.3% (using augmented data). The convolutional neural network has been applied to detect the autism spectrum disorder using the ABIDE dataset [ 11 ] and has achieved 70.22% accuracy using fewer parameters. The authors pointed out four essential regions for ASD classification: C115, C188, C247, and C326 for the CC400 functional parcellation.…”

Section: Literature Reviewmentioning

confidence: 99%

An Autoencoder-Based Deep Learning Classifier for Efficient Diagnosis of Autism

Sewani

Kashef

2020

Children

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Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by a lack of social communication and social interaction. Autism is a mental disorder investigated by social and computational intelligence scientists utilizing advanced technologies such as machine learning models to enhance clinicians’ ability to provide robust diagnosis and prognosis of autism. However, with dynamic changes in autism behaviour patterns, these models’ quality and accuracy have become a great challenge for clinical practitioners. We applied a deep neural network learning on a large brain image dataset obtained from ABIDE (autism brain imaging data exchange) to provide an efficient diagnosis of ASD, especially for children. Our deep learning model combines unsupervised neural network learning, an autoencoder, and supervised deep learning using convolutional neural networks. Our proposed algorithm outperforms individual-based classifiers measured by various validations and assessment measures. Experimental results indicate that the autoencoder combined with the convolution neural networks provides the best performance by achieving 84.05% accuracy and Area under the Curve (AUC) value of 0.78.

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“…Then a single layer perceptron and an autoencoder was used to classify the data on 1035 participants that resulted in 80% overall accuracy. A CNN based deep neural network and a Multichannel based attentional neural network for the classification of ASD were proposed in [ 50 ] and [ 51 ] respectively with promising results.…”

Section: Related Research Workmentioning

confidence: 99%

A Three-Stage Teacher, Student Neural Networks and Sequential Feed Forward Selection-Based Feature Selection Approach for the Classification of Autism Spectrum Disorder

et al. 2020

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Autism disorder, generally known as Autism Spectrum Disorder (ASD) is a brain disorder characterized by lack of communication skills, social aloofness and repetitions in the actions in the patients, which is affecting millions of the people across the globe. Accurate identification of autistic patients is considered a challenging task in the domain of brain disorder science. To address this problem, we have proposed a three-stage feature selection approach for the classification of ASD on the preprocessed Autism Brain Imaging Data Exchange (ABIDE) rs-fMRI Dataset. In the first stage, a large neural network which we call a “Teacher ” was trained on the correlation-based connectivity matrix to learn the latent representation of the input. In the second stage an autoencoder which we call a “Student” autoencoder was given the task to learn those trained “Teacher” embeddings using the connectivity matrix input. Lastly, an SFFS-based algorithm was employed to select the subset of most discriminating features between the autistic and healthy controls. On the combined site data across 17 sites, we achieved the maximum 10-fold accuracy of 82% and for the individual site-wise data, based on 5-fold accuracy, our results outperformed other state of the art methods in 13 out of the total 17 site-wise comparisons.

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Automated Detection of Autism Spectrum Disorder Using a Convolutional Neural Network

Cited by 175 publications

References 40 publications

Classifying Autism Spectrum Disorder Using the Temporal Statistics of Resting-State Functional MRI Data With 3D Convolutional Neural Networks

Classifying Autism Spectrum Disorder Using the Temporal Statistics of Resting-State Functional MRI Data With 3D Convolutional Neural Networks

An Autoencoder-Based Deep Learning Classifier for Efficient Diagnosis of Autism

A Three-Stage Teacher, Student Neural Networks and Sequential Feed Forward Selection-Based Feature Selection Approach for the Classification of Autism Spectrum Disorder

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