Automatic Classification of Autism Spectrum Disorder (ASD) from Brain MR Images Based on Feature Optimization and Machine Learning

Lohar, Mamata; Chorage, S. S.

doi:10.1109/smartgencon51891.2021.9645785

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…In the field of image processing, the K-Nearest Neighbor (K-NN) algorithm [30] may be used in a range of situations. The three most important aspects of this algorithm are a list of training examples that have been labelled, and a measurement that establishes a distance between the test set and the training set's example.…”

Section: Machine Learning Modelmentioning

confidence: 99%

An Integrated Statistical and Clinically Applicable Machine Learning Framework for the Detection of Autism Spectrum Disorder

et al. 2023

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Autism Spectrum Disorder (ASD) is a neurological impairment condition that severely impairs cognitive, linguistic, object recognition, interpersonal, and communication skills. Its main cause is genetic, and early treatment and identification can reduce the patient’s expensive medical costs and lengthy examinations. We developed a machine learning (ML) architecture that is capable of effectively analysing autistic children’s datasets and accurately classifying and identifying ASD traits. We considered the ASD screening dataset of toddlers in this study. We utilised the SMOTE method to balance the dataset, followed by feature transformation and selection methods. Then, we utilised several classification techniques in conjunction with a hyperparameter optimisation approach. The AdaBoost method yielded the best results among the classifiers. We employed ML and statistical approaches to identify the most crucial characteristics for the rapid recognition of ASD patients. We believe our proposed framework could be useful for early diagnosis and helpful for clinicians.

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Section: Machine Learning Modelmentioning

confidence: 99%

An Integrated Statistical and Clinically Applicable Machine Learning Framework for the Detection of Autism Spectrum Disorder

et al. 2023

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“…The significance of early diagnosing drew the attention of researchers towards using different machine learning-based procedures [16]. Therefore, early and accurate detection of ASD is required, which will help in treatment planning with the patient history and different medical tests, the brain MR scans can proceed towards ASD controls [17]. Rather than speech development only, the focus of therapy will be on successful communication in its broadest meaning.…”

mentioning

confidence: 99%

Deep Mobile Linguistic Therapy for Patients with ASD

Castellanos

Liu

Shi

2022

IJERPH

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Autistic spectrum disorder (ASD) is one of the most complex groups of neurobehavioral and developmental conditions. The reason is the presence of three different impaired domains, such as social interaction, communication, and restricted repetitive behaviors. Some children with ASD may not be able to communicate using language or speech. Many experts propose that continued therapy in the form of software training in this area might help to bring improvement. In this work, we propose a design of software speech therapy system for ASD. We combined different devices, technologies, and features with techniques of home rehabilitation. We used TensorFlow for Image Classification, ArKit for Text-to-Speech, Cloud Database, Binary Search, Natural Language Processing, Dataset of Sentences, and Dataset of Images with two different Operating Systems designed for Smart Mobile devices in daily life. This software is a combination of different Deep Learning Technologies and makes Human–Computer Interaction Therapy very easy to conduct. In addition, we explain the way these were connected and put to work together. Additionally, we explain in detail the architecture of software and how each component works together as an integrated Therapy System. Finally, it allows the patient with ASD to perform the therapy anytime and everywhere, as well as transmitting information to a medical specialist.

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Residual graph transformer for autism spectrum disorder prediction

Wang,

Long,

et al. 2024

Computer Methods and Programs in Biomedicine

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Automatic Classification of Autism Spectrum Disorder (ASD) from Brain MR Images Based on Feature Optimization and Machine Learning

Cited by 5 publications

References 12 publications

An Integrated Statistical and Clinically Applicable Machine Learning Framework for the Detection of Autism Spectrum Disorder

An Integrated Statistical and Clinically Applicable Machine Learning Framework for the Detection of Autism Spectrum Disorder

Deep Mobile Linguistic Therapy for Patients with ASD

Residual graph transformer for autism spectrum disorder prediction

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