Classification of Adults with Autism Spectrum Disorder using Deep Neural Network

Misman, Muhammad Faiz; Samah, Azurah A.; Ezudin, Farah Aqilah; Majid, Hairuddin Abu; Shah, Zuraini Ali; Hashim, Haslina; Harun, Muhamad Farhin

doi:10.1109/aidas47888.2019.8970823

Cited by 26 publications

(11 citation statements)

References 2 publications

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“…If the task of graph classification is something that arose recently, application of it to the domain of brain networks is a growing line of research (see [10] for a comprehensive survey). Recently several researchers [25,26,38] have proposed the use of deep learning architectures for the same problem we tackle in this paper, i.e., brain networks classification. However, these methods suffer from the black-box curse as they have very little interpretability.…”

Section: Related Workmentioning

confidence: 99%

Explainable Classification of Brain Networks via Contrast Subgraphs

Lanciano

Bonchi

Gionis

2020

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Mining human-brain networks to discover patterns that can be used to discriminate between healthy individuals and patients affected by some neurological disorder, is a fundamental task in neuroscience. Learning simple and interpretable models is as important as mere classification accuracy. In this paper we introduce a novel approach for classifying brain networks based on extracting contrast subgraphs, i.e., a set of vertices whose induced subgraphs are dense in one class of graphs and sparse in the other. We formally define the problem and present an algorithmic solution for extracting contrast subgraphs. We then apply our method to a brain-network dataset consisting of children affected by Autism Spectrum Disorder and children Typically Developed. Our analysis confirms the interestingness of the discovered patterns, which match background knowledge in the neuroscience literature. Further analysis on other classification tasks confirm the simplicity, soundness, and high explainability of our proposal, which also exhibits superior classification accuracy, to more complex state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Explainable Classification of Brain Networks via Contrast Subgraphs

Lanciano

Bonchi

Gionis

2020

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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“…Although there is virtually no cure for this disorder [14], this factor has motivated researchers to analyze the disease and explore to improve its treatment methodology [16,41]. [64], this led to generating a motivation to develop algorithms and reach the highest results in terms of accuracy and use even hybrid algorithms. In these contexts, many known data mining algorithms have been used or developed to solve this issue [56], such as the Bayesian fuzzy neural network proposed that was used in the study [65].…”

Section: Improving Early Diagnosis and Treatmentmentioning

confidence: 99%

“…Three distinct behavioral conduct deficits define ASD: (1) social interaction deficits, (2) verbal and nonverbal communication skills impairments, and (3) the prevalence of recurrent nonfunctional activities. At the moment, no one brain or psychological explanation is capable of accounting for all three deficiencies concurrently [19,46,64]. ASD is a heterogeneous disorder [41].…”

Section: Concerns Of Asd Communication Skillsmentioning

confidence: 99%

Diagnosis-Based Hybridization of Multimedical Tests and Sociodemographic Characteristics of Autism Spectrum Disorder Using Artificial Intelligence and Machine Learning Techniques: A Systematic Review

Alqaysi

Albahri

Hamid

2022

International Journal of Telemedicine and Applications

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Autism spectrum disorder (ASD) is a complex neurobehavioral condition that begins in childhood and continues throughout life, affecting communication and verbal and behavioral skills. It is challenging to discover autism in the early stages of life, which prompted researchers to intensify efforts to reach the best solutions to treat this challenge by introducing artificial intelligence (AI) techniques and machine learning (ML) algorithms, which played an essential role in greatly assisting the medical and healthcare staff and trying to obtain the highest predictive results for autism spectrum disorder. This study is aimed at systematically reviewing the literature related to the criteria, including multimedical tests and sociodemographic characteristics in AI techniques and ML contributions. Accordingly, this study checked the Web of Science (WoS), Science Direct (SD), IEEE Xplore digital library, and Scopus databases. A set of 944 articles from 2017 to 2021 is collected to reveal a clear picture and better understand all the academic literature through a definitive collection of 40 articles based on our inclusion and exclusion criteria. The selected articles were divided based on similarity, objective, and aim evidence across studies. They are divided into two main categories: the first category is “diagnosis of ASD based on questionnaires and sociodemographic features” ( n = 39 ). This category contains a subsection that consists of three categories: (a) early diagnosis of ASD towards analysis, (b) diagnosis of ASD towards prediction, and (c) diagnosis of ASD based on resampling techniques. The second category consists of “diagnosis ASD based on medical and family characteristic features” ( n = 1 ). This multidisciplinary systematic review revealed the taxonomy, motivations, recommendations, and challenges of diagnosis ASD research in utilizing AI techniques and ML algorithms that need synergistic attention. Thus, this systematic review performs a comprehensive science mapping analysis and identifies the open issues that help accomplish the recommended solution of diagnosis ASD research. Finally, this study critically reviews the literature and attempts to address the diagnosis ASD research gaps in knowledge and highlights the available ASD datasets, AI techniques and ML algorithms, and the feature selection methods that have been collected from the final set of articles.

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“…After DL’s success in the ImageNet challenge in 2012, it only took 5 years for the first DL algorithm for medical imaging ( Krizhevsky et al, 2012 ). A deep neural network (DNN) ( Misman et al, 2019 ) has one or more hidden layers between the input and output layers. Each layer is made up of layers of nodes known as artificial neurons (see Figure 3B ).…”

Section: General Machine Learning Pipeline and Common Algorithms For ...mentioning

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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Classification of Adults with Autism Spectrum Disorder using Deep Neural Network

Cited by 26 publications

References 2 publications

Explainable Classification of Brain Networks via Contrast Subgraphs

Explainable Classification of Brain Networks via Contrast Subgraphs

Diagnosis-Based Hybridization of Multimedical Tests and Sociodemographic Characteristics of Autism Spectrum Disorder Using Artificial Intelligence and Machine Learning Techniques: A Systematic Review

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

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