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  • Open Access

    ARTICLE

    Multi-Scale Attention-Based Deep Neural Network for Brain Disease Diagnosis

    Yin Liang1,*, Gaoxu Xu1, Sadaqat ur Rehman2

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4645-4661, 2022, DOI:10.32604/cmc.2022.026999

    Abstract Whole brain functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used in the diagnosis of brain disorders such as autism spectrum disorder (ASD). Recently, an increasing number of studies have focused on employing deep learning techniques to analyze FC patterns for brain disease classification. However, the high dimensionality of the FC features and the interpretation of deep learning results are issues that need to be addressed in the FC-based brain disease classification. In this paper, we proposed a multi-scale attention-based deep neural network (MSA-DNN) model to classify FC patterns for the ASD diagnosis.… More >

  • Open Access

    ARTICLE

    Facial Action Coding and Hybrid Deep Learning Architectures for Autism Detection

    A. Saranya1,*, R. Anandan2

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1167-1182, 2022, DOI:10.32604/iasc.2022.023445

    Abstract Hereditary Autism Spectrum Disorder (ASD) is a neuron disorder that affects a person's ability for communication, interaction, and also behaviors. Diagnostics of autism are available throughout all stages of life, from infancy through adolescence and adulthood. Facial Emotions detection is considered to be the most parameter for the detection of Autismdisorders among the different categories of people. Propelled with a machine and deep learning algorithms, detection of autism disorder using facial emotions has reached a new dimension and has even been considered as the precautionary warning system for caregivers. Since Facial emotions are limited to only seven expressions, detection of… More >

  • Open Access

    REVIEW

    Future perspectives on cell therapy for autism spectrum disorder

    MAKOTO NABETANI1,*, TAKEO MUKAI2

    BIOCELL, Vol.46, No.4, pp. 873-879, 2022, DOI:10.32604/biocell.2022.018218

    Abstract Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by impairments in social communication, abnormal to absent verbal communication, the presence of repetitive stereotypic verbal and non-verbal behaviors and restricted interests, with onset early in life. We showed cognitive and behavioral characteristics of ASD by impairment of communication, cognition, perception, motor skills, executive function, theory of mind and emotion control. Recently, pathogenesis of immune pathology in the brains of individuals with ASD has been focused. New therapeutic approaches in the viewpoints of immune modulation and microglial function are logical for novel treatments for individuals with ASD. Cell therapies such… More >

  • Open Access

    ARTICLE

    Autism Spectrum Disorder Prediction by an Explainable Deep Learning Approach

    Anupam Garg1, Anshu Parashar1, Dipto Barman2, Sahil Jain3, Divya Singhal3, Mehedi Masud4, Mohamed Abouhawwash5,6,*

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1459-1471, 2022, DOI:10.32604/cmc.2022.022170

    Abstract Autism Spectrum Disorder (ASD) is a developmental disorder whose symptoms become noticeable in early years of the age though it can be present in any age group. ASD is a mental disorder which affects the communicational, social and non-verbal behaviors. It cannot be cured completely but can be reduced if detected early. An early diagnosis is hampered by the variation and severity of ASD symptoms as well as having symptoms commonly seen in other mental disorders as well. Nowadays, with the emergence of deep learning approaches in various fields, medical experts can be assisted in early diagnosis of ASD. It… More >

  • Open Access

    ARTICLE

    Autism Spectrum Disorder Diagnosis Using Ensemble ML and Max Voting Techniques

    A. Arunkumar1,*, D. Surendran2

    Computer Systems Science and Engineering, Vol.41, No.1, pp. 389-404, 2022, DOI:10.32604/csse.2022.020256

    Abstract Difficulty in communicating and interacting with other people are mainly due to the neurological disorder called autism spectrum disorder (ASD) diseases. These diseases can affect the nerves at any stage of the human being in childhood, adolescence, and adulthood. ASD is known as a behavioral disease due to the appearances of symptoms over the first two years that continue until adulthood. Most of the studies prove that the early detection of ASD helps improve the behavioral characteristics of patients with ASD. The detection of ASD is a very challenging task among various researchers. Machine learning (ML) algorithms still act very… More >

  • Open Access

    ARTICLE

    Combined Signal Processing Based Techniques and Feed Forward Neural Networks for Pathological Voice Detection and Classification

    T. Jayasree1,*, S.Emerald Shia2

    Sound & Vibration, Vol.55, No.2, pp. 141-161, 2021, DOI:10.32604/sv.2021.011734

    Abstract This paper presents the pathological voice detection and classification techniques using signal processing based methodologies and Feed Forward Neural Networks (FFNN). The important pathological voices such as Autism Spectrum Disorder (ASD) and Down Syndrome (DS) are considered for analysis. These pathological voices are known to manifest in different ways in the speech of children and adults. Therefore, it is possible to discriminate ASD and DS children from normal ones using the acoustic features extracted from the speech of these subjects. The important attributes hidden in the pathological voices are extracted by applying different signal processing techniques. In this work, three… More >

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