Classification and Biomarker Exploration of Autism Spectrum Disorders Based on Recurrent Attention Model

Ke, Fengkai; Yang, Rui

doi:10.1109/access.2020.3038479

Cited by 14 publications

(8 citation statements)

References 35 publications

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“…On detection of autism state, children are classified with severity of meltdown. Attention layer of the model process the feature embedding to produce the optimal severity state of the children with meltdown with high efficiency [31].…”

Section: Autism Spectrum Disorder and Meltdown Detection Using Recurr...mentioning

confidence: 99%

Enhancing Autism Severity Classification: Integrating LSTM into CNNs for Multisite Meltdown Grading

Alam,

Raja,

Gulzar

et al. 2023

IJACSA

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Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by deficits in social interaction, verbal and non-verbal communication, and is often associated with cognitive and neurobehavioral challenges. Timely screening and diagnosis of ASD are crucial for early educational planning, treatment, family support, and timely medical intervention. Manual diagnostic methods are time-consuming and labor-intensive, underscoring the need for automated approaches to assist caretakers and parents. While various researchers have employed machine learning and deep learning techniques for ASD diagnosis, existing models often fall short in capturing the complexity of multisite meltdowns and fully leveraging the interdependence among these meltdowns for severity assessment in acquired facial images of children, hindering the development of a comprehensive grading system. This paper introduces a novel approach using a Long Short Term Memory (LSTM) integrated Convolution Neural Network (CNN) designed to identify multisite meltdowns and exploit their interdependence for severity assessment in ASD. The process begins with image pre-processing, involving discrete convolution filters for noise removal and contrast enhancement to improve image quality. The enhanced image then undergoes instance segmentation using the Segment Anything model to identify significant regions in the child's image. The segmented region is subjected to principal component analysis for feature extraction, and these features are utilized by the LSTM-integrated CNN for meltdown detection and severity classification. The model is trained using children's images extracted from videos, and testing is performed on videos captured during children's observations. Performance analysis reveals superior results, with a training accuracy of 88% and validation accuracy of 84%, outperforming conventional methods. This innovative approach not only enhances the efficiency of ASD diagnosis but also provides a more nuanced understanding of multisite meltdowns and their impact on severity, contributing to the development of a robust grading system.

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Section: Autism Spectrum Disorder and Meltdown Detection Using Recurr...mentioning

confidence: 99%

Enhancing Autism Severity Classification: Integrating LSTM into CNNs for Multisite Meltdown Grading

Alam,

Raja,

Gulzar

et al. 2023

IJACSA

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“…ROI detection algorithms fall into four categories: (1) based on changes in voxel values, like edge detection algorithms; (2) based on human-computer interaction. (3) those that use human visual characteristics, such as color detection algorithms; (4) DL-dependent, like Recurrent Attention Model (RAM) and Class Activation Mapping (CAM) ( Ke and Yang, 2020 ).…”

Section: Structural Magnetic Reasoning Imaging and Features Extractionmentioning

confidence: 99%

“…One study ( Ke and Yang, 2020 ) developed a RAM-based approach for identifying ASD using sMRI data. To improve the convergence of the Policy Gradient approach used in conventional RAM, they developed a Deep Deterministic Policy Gradient-RAM (DDPG-RAM) model and a Gaussian sampling-based priority experience replay (PER) algorithm.…”

Section: Highlighted Researchmentioning

confidence: 99%

Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging

Bahathiq

Banjar

Bamaga

et al. 2022

Front. Neuroinform.

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Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects approximately 1% of the population and causes significant burdens. ASD’s pathogenesis remains elusive; hence, diagnosis is based on a constellation of behaviors. Structural magnetic resonance imaging (sMRI) studies have shown several abnormalities in volumetric and geometric features of the autistic brain. However, inconsistent findings prevented most contributions from being translated into clinical practice. Establishing reliable biomarkers for ASD using sMRI is crucial for the correct diagnosis and treatment. In recent years, machine learning (ML) and specifically deep learning (DL) have quickly extended to almost every sector, notably in disease diagnosis. Thus, this has led to a shift and improvement in ASD diagnostic methods, fulfilling most clinical diagnostic requirements. However, ASD discovery remains difficult. This review examines the ML-based ASD diagnosis literature over the past 5 years. A literature-based taxonomy of the research landscape has been mapped, and the major aspects of this topic have been covered. First, we provide an overview of ML’s general classification pipeline and the features of sMRI. Next, representative studies are highlighted and discussed in detail with respect to methods, and biomarkers. Finally, we highlight many common challenges and make recommendations for future directions. In short, the limited sample size was the main obstacle; Thus, comprehensive data sets and rigorous methods are necessary to check the generalizability of the results. ML technologies are expected to advance significantly in the coming years, contributing to the diagnosis of ASD and helping clinicians soon.

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“…During the assessment process for autism, the subject is subjected to observation, and the examiner assigns scores based on their observations. The autism diagnostic interview-revised (ADI-R) is a commonly utilized structured interview that involves gathering information from parents regarding the developmental history of the individual [10]. The diagnostic tools used in this context have an observational nature, which increases the likelihood of generating false-positive results.…”

Section: Introductionmentioning

confidence: 99%

Autism spectrum disorder identification with multi-site functional magnetic resonance imaging

Lylath,

Rananavare

2024

IJ-AI

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Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by enduring difficulties in social interaction and communication. People analyzed with ASD may display repetitive behaviors and limited interests. Autism is classified as a spectrum disorder, implying that the symptom intensity might range from mild to severe depending on the individual. To detect ASD in this paper an attribute feature graph approach is designed by using the stastical dependencies features that necessarily accomplish the diagnosis of ASD. In the first phase the features extracted are designed based on the functional magnetic resonance imaging (fMRI) data, in the next-step the attribute feature graph layer learns the features of the node information of various nodes by ASD classification. Further, in the third step, it is employed to independently extract distinguishing features from the functional connectivity matrices of the brain that are derived from fMRI. The custom convolutional neural network (CNN) used in this study is trained on a comprehensive dataset comprising individuals diagnosed with ASD and typically developing individuals. In the fourth stage, a prototype learning is developed to augment the classification performance of the custom-CNN. The experimental analysis further carried out states that the proposed model works efficiently in comparison with the existing system.

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Classification and Biomarker Exploration of Autism Spectrum Disorders Based on Recurrent Attention Model

Cited by 14 publications

References 35 publications

Enhancing Autism Severity Classification: Integrating LSTM into CNNs for Multisite Meltdown Grading

Enhancing Autism Severity Classification: Integrating LSTM into CNNs for Multisite Meltdown Grading

Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging

Autism spectrum disorder identification with multi-site functional magnetic resonance imaging

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