An Effective Sparse Autoencoders based Deep Learning Framework for fMRI Scans Classification

Mahmoud, Abeer M.; Karamti, Hanen; Alrowais, Fadwa

doi:10.5220/0009397605400547

Cited by 5 publications

(5 citation statements)

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“…Autoencoders are applied for feature reduction and feature learning [8][9][10][11] . Many studies 8,10 combine autoencoders with supervised and unsupervised learning to realize better feature extraction and classification performance.…”

Section: Models Based On Autoencodermentioning

confidence: 99%

“…Heinsfeld et al 9 adopted stacked denoising autoencoders to extract features implemented in the model's unsupervised and supervised stages. Mahmoud et al 10 also used an unsupervised algorithm, applying two sparse autoencoders to extract features, while an additional autoencoder was employed as a supervised classifier. Both sets of experiments implement data preprocessing in an unsupervised stage.…”

Section: Models Based On Autoencodermentioning

confidence: 99%

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Comparison of different classification methods for autism spectrum diagnosis

Liu

Wang²,

Tian³

et al. 2022

Fourteenth International Conference on Digital Image Processing (ICDIP 2022)

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Studies have found autism spectrum disorder is a diffuse developmental disease of the central nervous system. The majority of autism cases result from a combination of genetic predisposition and environmental factors that influence early brain development, despite a few being caused by genes alone. Traditional diagnosis of autism spectrum disorder is usually through interviews and questionnaires, which takes plenty of time and might be misdiagnosed. The primary purpose of this study is to compare different classification methods for distinguishing autism spectrum disorder from typical development by machine learning and deep learning in recent years. The experiments are conducted to discuss their strengths and weaknesses, which, in turn, results are presented for further research.

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Section: Models Based On Autoencodermentioning

confidence: 99%

Section: Models Based On Autoencodermentioning

confidence: 99%

Comparison of different classification methods for autism spectrum diagnosis

Liu

Wang²,

Tian³

et al. 2022

Fourteenth International Conference on Digital Image Processing (ICDIP 2022)

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“…Abeer, et al, in [2], proposed a hybrid unsupervised and supervised framework that is based on the sparse autoencoders controlling and summarization of features and obtained better results than literature on same data domain. Xi.…”

Section: State Of the Artmentioning

confidence: 99%

“…The encoder f (Ie) function is producing an abstracted version of the input Ie by a hidden layer he; while the function of the decoder (Oe(f(Ie)) is the reconstruction process of the original input from the layer he with a minim loss function: L (Ie, Oe(f(Ie))). The contractive autoencoder [31] used tiny derivatives and sum to an explicit regularize β(he) to the hidden layer he and control the encoder to minimize the regularize in (2). This forced the model to discover any slight variations of input values (ß(he): is the squared Frobenius norm [30] of the Jacobian partial derivative matrix, α: is a free parameter) ß (ℎ ) =α ( )…”

Section: Contractive Autoencodermentioning

confidence: 99%

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Classifying a type of brain disorder in children: an effective fMRI based deep attempt

Mahmoud¹,

Karamti²

2021

IJEECS

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<span>Recent advanced intelligent learning approaches that are based on using neural networks in medical diagnosing increased researcher expectations. In fact, the literature proved a straight-line relation of the exact needs and the achieved results. Accordingly, it encouraged promising directions of applying these approaches toward saving time and efforts. This paper proposes a novel hybrid deep learning framework that is based on the restricted boltzmann machines (RBM) and the contractive autoencoder (CA) to classify the brain disorder and healthy control cases in children less than 12 years. The RBM focuses on obtaining the discriminative set of high guided features in the classification process, while the CA provides the regularization and the robustness of features for optimal objectives. The proposed framework diagnosed children with autism recording accuracy of 91, 14% and proved enhancement compared to literature.</span>

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Automated diagnosis of autism: State of the art

Valizadeh

Moassefi

Nakhostin-Ansari

et al. 2021

Preprint

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Objective: To determine the diagnostic accuracy of the applied machine learning algorithms for the diagnosis of autism spectrum disorder (ASD) based on structural magnetic resonance imaging (sMRI), resting-state functional MRI (rs-fMRI), and electroencephalography (EEG). Methods: We will include cross-sectional studies (both single-gates and two-gates) that have evaluated the diagnostic accuracy of machine learning algorithms on the sMRI data of ASD patients regardless of age, sex, and ethnicity. On the 22nd of May 2021, we searched Embase, MEDLINE, APA PsycINFO, IEEE Xplore, Scopus, and Web of Science for eligible studies. We also searched grey literature within various sources. We will use an adapted version of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool to assess the risk of bias and applicability. Data will be synthesized using the relatively new Split Component Synthesis (SCS) method. We plan to assess heterogeneity using the I2 statistics and assess publication bias using trim and fill tests combined with ln DOR. Certainty of evidence will be assessed using the GRADE approach for diagnostic studies. Funding: These studies are funded by Sports Medicine Research Center, Tehran, Iran. Registration: PROSPERO submission IDs: 262575, 262825, and 262831.

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An Effective Sparse Autoencoders based Deep Learning Framework for fMRI Scans Classification

Cited by 5 publications

References 0 publications

Comparison of different classification methods for autism spectrum diagnosis

Comparison of different classification methods for autism spectrum diagnosis

Classifying a type of brain disorder in children: an effective fMRI based deep attempt

Automated diagnosis of autism: State of the art

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