Bag-of-Features Model for ASD fMRI Classification using SVM

Ahammed, Shale; Niu, Sijie; Ahmed, Rishad; Dong, Jiwen; Gao, Xizhan; Chen, Yuehui

doi:10.1109/acctcs52002.2021.00019

Cited by 10 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our training samples are few (51 ASDs vs. 51 HCs), but the number of features is large. Thus, a supervised linear SVM classifier embedded in the LIBSVM toolbox (Chang & Lin, 2011) was chosen, which is good at dealing with small sample data and it allows direct extraction of feature weight (Ahammed et al, 2021a). In the linear SVM, only parameter C (penalty coefficient) takes the default value (i.e., C = 1).…”

Section: Methodsmentioning

confidence: 99%

Identification and analysis of autism spectrum disorder via large‐scale dynamic functional network connectivity

et al. 2023

View full text Add to dashboard Cite

Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder with severe cognitive impairment. Several studies have reported that brain functional network connectivity (FNC) has great potential for identifying ASD from healthy control (HC) and revealing the relationships between the brain and behaviors of ASD. However, few studies have explored dynamic large‐scale FNC as a feature to identify individuals with ASD. This study used a time‐sliding window method to study the dynamic FNC (dFNC) on the resting‐state fMRI. To avoid arbitrarily determining the window length, we set a window length range of 10–75 TRs (TR = 2 s). We constructed linear support vector machine classifiers for all window length conditions. Using a nested 10‐fold cross‐validation framework, we obtained a grand average accuracy of 94.88% across window length conditions, which is higher than those reported in previous studies. In addition, we determined the optimal window length using the highest classification accuracy of 97.77%. Based on the optimal window length, we found that the dFNCs were located mainly in dorsal and ventral attention networks (DAN and VAN) and exhibited the highest weight in classification. Specifically, we found that the dFNC between DAN and temporal orbitofrontal network (TOFN) was significantly negatively correlated with social scores of ASD. Finally, using the dFNCs with high classification weights as features, we construct a model to predict the clinical score of ASD. Overall, our findings demonstrated that the dFNC could be a potential biomarker to identify ASD and provide new perspectives to detect cognitive changes in ASD.

show abstract

Section: Methodsmentioning

confidence: 99%

Identification and analysis of autism spectrum disorder via large‐scale dynamic functional network connectivity

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The classification was performed by an SVM model. Ahammed et al [113] used the extracted features from ROI-based FC matrices with the bag-of-features extraction (BoF) method. The SVM was used as a classifier for identifying ASD and HC subjects.…”

Section: The Role Of Ai For Asd Diagnosis Using Fmrimentioning

confidence: 99%

Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey

Helmy,

Elnakib,

ElNakieb

et al. 2023

Biomedicines

View full text Add to dashboard Cite

Autism spectrum disorder (ASD) is a wide range of diseases characterized by difficulties with social skills, repetitive activities, speech, and nonverbal communication. The Centers for Disease Control (CDC) estimates that 1 in 44 American children currently suffer from ASD. The current gold standard for ASD diagnosis is based on behavior observational tests by clinicians, which suffer from being subjective and time-consuming and afford only late detection (a child must have a mental age of at least two to apply for an observation report). Alternatively, brain imaging—more specifically, magnetic resonance imaging (MRI)—has proven its ability to assist in fast, objective, and early ASD diagnosis and detection. With the recent advances in artificial intelligence (AI) and machine learning (ML) techniques, sufficient tools have been developed for both automated ASD diagnosis and early detection. More recently, the development of deep learning (DL), a young subfield of AI based on artificial neural networks (ANNs), has successfully enabled the processing of brain MRI data with improved ASD diagnostic abilities. This survey focuses on the role of AI in autism diagnostics and detection based on two basic MRI modalities: diffusion tensor imaging (DTI) and functional MRI (fMRI). In addition, the survey outlines the basic findings of DTI and fMRI in autism. Furthermore, recent techniques for ASD detection using DTI and fMRI are summarized and discussed. Finally, emerging tendencies are described. The results of this study show how useful AI is for early, subjective ASD detection and diagnosis. More AI solutions that have the potential to be used in healthcare settings will be introduced in the future.

show abstract

“…In 2014, the overall prevalence of autism was estimated at 16.8 per 1, 000 8-year-old children, and the prevalence of ASD reached nearly 3% in some communities (Baio et al, 2018 ). In recent years, people have been worried about the increased prevalence of ASD in children (Hodges et al, 2020 ; Ahammed et al, 2021 ). However, the current diagnosis of autism is highly dependent on traditional behavioral symptoms, which are usually subjective and can easily lead to neglect early symptoms and misdiagnosis (American Psychiatric Association, 2013 ; Lord et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Graph Representation Learning for Autism Identification With Functional MRI

Wang¹,

Cao²,

Qiao³

et al. 2022

Front. Neuroinform.

View full text Add to dashboard Cite

Resting-state functional MRI (rs-fMRI) has been widely used for the early diagnosis of autism spectrum disorder (ASD). With rs-fMRI, the functional connectivity networks (FCNs) are usually constructed for representing each subject, with each element representing the pairwise relationship between brain region-of-interests (ROIs). Previous studies often first extract handcrafted network features (such as node degree and clustering coefficient) from FCNs and then construct a prediction model for ASD diagnosis, which largely requires expert knowledge. Graph convolutional networks (GCNs) have recently been employed to jointly perform FCNs feature extraction and ASD identification in a data-driven manner. However, existing studies tend to focus on the single-scale topology of FCNs by using one single atlas for ROI partition, thus ignoring potential complementary topology information of FCNs at different spatial scales. In this paper, we develop a multi-scale graph representation learning (MGRL) framework for rs-fMRI based ASD diagnosis. The MGRL consists of three major components: (1) multi-scale FCNs construction using multiple brain atlases for ROI partition, (2) FCNs representation learning via multi-scale GCNs, and (3) multi-scale feature fusion and classification for ASD diagnosis. The proposed MGRL is evaluated on 184 subjects from the public Autism Brain Imaging Data Exchange (ABIDE) database with rs-fMRI scans. Experimental results suggest the efficacy of our MGRL in FCN feature extraction and ASD identification, compared with several state-of-the-art methods.

show abstract

Bag-of-Features Model for ASD fMRI Classification using SVM

Cited by 10 publications

References 14 publications

Identification and analysis of autism spectrum disorder via large‐scale dynamic functional network connectivity

Identification and analysis of autism spectrum disorder via large‐scale dynamic functional network connectivity

Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey

Multi-Scale Graph Representation Learning for Autism Identification With Functional MRI

Contact Info

Product

Resources

About