Detection of Autism Spectrum Disorder using fMRI Functional Connectivity with Feature Selection and Deep Learning

Zhang, Jin; Feng, Fan; Han, Tianyi; Gong, Xiaoli; Duan, Feng

doi:10.1007/s12559-021-09981-z

Cited by 36 publications

(26 citation statements)

References 31 publications

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“…Also, for development the structural as well as the subjects' phenotypic data to the functional structures were combined. Zhang et al (2022) developed ASD diagnosis based on F-score feature selection and deep learning technique. Here, the path length was reduced to enhance the result of this approach.…”

Section: Literature Surveymentioning

confidence: 99%

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Feedback Artificial Tree‐Anti Corona Virus Optimization enabled deep learning for detecting autism spectrum disorder

Arumugam¹,

Velrajkumar

Sriramakrishnan

et al. 2022

Expert Systems

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Autism spectrum disorder (ASD) is an umbrella term for a number of neurodevelopmental conditions with many heterogeneous behavioural indications. Recent medical imaging approaches use functional Magnetic Resonance Imaging (fMRI) for human recognition of the various neurological syndromes. However, these traditional techniques are time consuming and expensive. Thus, in this research, an optimization assisted deep learning technique, named Feedback Artificial Virus Optimization (FAVO)‐based deep residual network (DRN), is developed. FAVO‐based DRN is designed to incorporate the Feedback Artificial Tree (FAT) algorithm with Anti Corona Virus Optimization (ACVO). First, Region‐Of‐Interest extraction is carried out using thresholding techniques with nub region extraction completed using the proposed FAVO algorithm. ASD classification is then carried out using a DRN classifier. Evaluation of the proposal uses the ABIDE‐1 and ABIDE‐2 datasets. The developed FAVO algorithm attains better accuracy, sensitivity, and specificity of 0.9214, 0.9365, and 0.9142, respectively, by considering ABIDE‐2 dataset.

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Section: Literature Surveymentioning

confidence: 99%

“…Zhang et al (2022) developed ASD diagnosis based on F ‐score feature selection and deep learning technique. Here, the path length was reduced to enhance the result of this approach.…”

Section: Literature Surveymentioning

confidence: 99%

Feedback Artificial Tree‐Anti Corona Virus Optimization enabled deep learning for detecting autism spectrum disorder

Arumugam¹,

Velrajkumar

Sriramakrishnan

et al. 2022

Expert Systems

View full text Add to dashboard Cite

show abstract

“…Finally, the scaled FC with the attention mask X mask represents the connection that requires more focus for accurate diagnosis. Unlike the feature selection methods that apply to all individuals equally [36]- [38], M varies with the functional properties of individuals, such that each individual has different subsets of attentive features.…”

Section: B Adaptive Attention Networkmentioning

confidence: 99%

Prototype Learning of Inter-network Connectivity for ASD Diagnosis and Personalized Analysis

Kang¹,

Heo²,

Suk³

2022

Lecture Notes in Computer Science

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To understand the biological characteristics of neurological disorders with functional connectivity (FC), recent studies have widely utilized deep learning-based models to identify the disease and conducted post-hoc analyses via explainable models to discover diseaserelated biomarkers. Most existing frameworks consist of three stages, namely, feature selection, feature extraction for classification, and analysis, where each stage is implemented separately. However, if the results at each stage lack reliability, it can cause misdiagnosis and incorrect analysis in afterward stages. In this study, we propose a novel unified framework that systemically integrates diagnoses (i.e., feature selection and feature extraction) and explanations. Notably, we devised an adaptive attention network as a feature selection approach to identify individualspecific disease-related connections. We also propose a functional network relational encoder that summarizes the global topological properties of FC by learning the internetwork relations without pre-defined edges between functional networks. Last but not least, our framework provides a novel explanatory power for neuroscientific interpretation, also termed counter-condition analysis. We simulated the FC that reverses the diagnostic information (i.e., counter-condition FC): converting a normal brain to be abnormal and vice versa. We validated the effectiveness of our framework by using two large resting-state functional magnetic resonance imaging (fMRI) datasets, Autism Brain Imaging Data Exchange (ABIDE) and REST-meta-MDD, and demonstrated that our framework outperforms other competing methods for disease identification. Furthermore, we analyzed the disease-related neurological patterns based on counter-condition analysis.

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“…Having low sensation than other people in such matters are: bright, sound, and so forth. Improving the affected people's pain with autism spectrum disorder is to less the infection by detecting earlier 17,18 . Moreover, few steps are taken for treating ASD; its syndromes are recognized via opinion.…”

Section: Introductionmentioning

confidence: 99%

“…Improving the affected people's pain with autism spectrum disorder is to less the infection by detecting earlier. 17,18 Moreover, few steps are taken for treating ASD; its syndromes are recognized via opinion. Autism spectrum disorder syndromes are identified with the aged and young children by their parents.…”

mentioning

confidence: 99%

An effective autism spectrum disorder screening method using machine learning classification techniques

Balasubramanian,

Gururaj,

Gayatri

2023

Concurrency and Computation

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SummaryNowadays, autism spectrum disorder (ASD) is one of the fastest‐growing developmental disorders globally. Screening test consumes more time and expensive to detect the autism signs. Due to advances in artificial intelligence and machine learning (ML), Autism can be predicted at the initial stage. Numerous researches have been conducted using different methods, but none of these researches presented any anticipated results about the capability to predict autism traits under different age groups. Therefore, this paper proposes an effectual prediction method based upon ML strategy to develop a mobile application for predicting ASD of any age people. The autism prediction model is developed by five ML classifiers, such as Gaussian Naive Bayes, Decision Tree Classifier, K‐Nearest Neighbors (KNN), Multinomial Logistic Regression (MLR), Support Vector Machine (SVM) and also a mobile application is developed using proposed prediction method. The proposed method is analyzed with IAPQ records gathered from certain areas in Erode district, Tamil Nadu, India. From the analysis, the SVM classifier achieves maximum sensitivity of 23.14%, 6.04%, 5.89%, 11.03% than other classifiers, like Gaussian Naive Bayes, Decision Tree Classifier, KNN, and MLR.

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Detection of Autism Spectrum Disorder using fMRI Functional Connectivity with Feature Selection and Deep Learning

Cited by 36 publications

References 31 publications

Feedback Artificial Tree‐Anti Corona Virus Optimization enabled deep learning for detecting autism spectrum disorder

Feedback Artificial Tree‐Anti Corona Virus Optimization enabled deep learning for detecting autism spectrum disorder

Prototype Learning of Inter-network Connectivity for ASD Diagnosis and Personalized Analysis

An effective autism spectrum disorder screening method using machine learning classification techniques

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