Addressing the challenge of dataset acquisition for ASD diagnosis with deep learning-based neural networks

The abstract examines the need for prompt and accurate diagnosis of Autism Spectrum Disorder (ASD), a neurodevelopmental disorder characterized by difficulties in social communication and repetitive behaviors. Early diagnosis is crucial in facilitating appropriate intervention and support, thereby...

Full description

Saved in:
Bibliographic Details
Main Authors: Alam, Mohammad Shafiul, Rashid, Muhammad Mahbubur, Ali, Mohammad Yeakub, Yvette, Susiapan
Format: Proceeding Paper
Language:English
English
Published: AIP publishing 2024
Subjects:
Online Access:http://irep.iium.edu.my/115389/7/115389_Addressing%20the%20challenge%20of%20dataset.pdf
http://irep.iium.edu.my/115389/8/115389_Addressing%20the%20challenge%20of%20dataset_Scopus.pdf
http://irep.iium.edu.my/115389/
https://pubs.aip.org/aip/acp/article-abstract/3161/1/020122/3310611/Addressing-the-challenge-of-dataset-acquisition?redirectedFrom=fulltext
https://doi.org/10.1063/5.0229866
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Universiti Islam Antarabangsa Malaysia
Language: English
English
id my.iium.irep.115389
record_format dspace
spelling my.iium.irep.1153892024-10-30T01:48:02Z http://irep.iium.edu.my/115389/ Addressing the challenge of dataset acquisition for ASD diagnosis with deep learning-based neural networks Alam, Mohammad Shafiul Rashid, Muhammad Mahbubur Ali, Mohammad Yeakub Yvette, Susiapan TK1001 Production of electric energy. Powerplants The abstract examines the need for prompt and accurate diagnosis of Autism Spectrum Disorder (ASD), a neurodevelopmental disorder characterized by difficulties in social communication and repetitive behaviors. Early diagnosis is crucial in facilitating appropriate intervention and support, thereby substantially improving the potential for children's social and cognitive development. The conventional diagnostic approaches for ASD are often characterized by lengthy durations, high expenses, and susceptibility to inconsistencies across evaluators. These challenges are primarily due to the complex nature of the illness, which requires a complete evaluation encompassing several aspects such as behavior, neurology, and other relevant characteristics. Given the obstacles mentioned above, applying deep learning methodologies has emerged as a potentially fruitful approach for diagnosing ASD. These techniques demonstrate exceptional proficiency in identifying significant patterns and characteristics from diverse datasets, making a valuable contribution to developing more accurate and effective diagnostic models. This comprehensive literature review examines recent studies on the diagnosis of ASD, particularly emphasizing the various types of datasets employed in deep learningbased methods. The study covers various datasets that include behavioral data, capturing intricate details of social interaction and communication patterns, neuroimaging data that provide insights into the structure and function of the brain, and datasets that incorporate genetic and clinical assessments. The paper provides a more in-depth analysis of the challenges faced while acquiring datasets, which include issues related to data reliability, the adequacy of sample sizes, and the variability present within the datasets AIP publishing 2024-08-30 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/115389/7/115389_Addressing%20the%20challenge%20of%20dataset.pdf application/pdf en http://irep.iium.edu.my/115389/8/115389_Addressing%20the%20challenge%20of%20dataset_Scopus.pdf Alam, Mohammad Shafiul and Rashid, Muhammad Mahbubur and Ali, Mohammad Yeakub and Yvette, Susiapan (2024) Addressing the challenge of dataset acquisition for ASD diagnosis with deep learning-based neural networks. In: 5th International Conference on Sustainable Innovation in Engineering and Technology 2023, 16 August 2023, Kuala Lumpur, Malaysia. https://pubs.aip.org/aip/acp/article-abstract/3161/1/020122/3310611/Addressing-the-challenge-of-dataset-acquisition?redirectedFrom=fulltext https://doi.org/10.1063/5.0229866
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic TK1001 Production of electric energy. Powerplants
spellingShingle TK1001 Production of electric energy. Powerplants
Alam, Mohammad Shafiul
Rashid, Muhammad Mahbubur
Ali, Mohammad Yeakub
Yvette, Susiapan
Addressing the challenge of dataset acquisition for ASD diagnosis with deep learning-based neural networks
description The abstract examines the need for prompt and accurate diagnosis of Autism Spectrum Disorder (ASD), a neurodevelopmental disorder characterized by difficulties in social communication and repetitive behaviors. Early diagnosis is crucial in facilitating appropriate intervention and support, thereby substantially improving the potential for children's social and cognitive development. The conventional diagnostic approaches for ASD are often characterized by lengthy durations, high expenses, and susceptibility to inconsistencies across evaluators. These challenges are primarily due to the complex nature of the illness, which requires a complete evaluation encompassing several aspects such as behavior, neurology, and other relevant characteristics. Given the obstacles mentioned above, applying deep learning methodologies has emerged as a potentially fruitful approach for diagnosing ASD. These techniques demonstrate exceptional proficiency in identifying significant patterns and characteristics from diverse datasets, making a valuable contribution to developing more accurate and effective diagnostic models. This comprehensive literature review examines recent studies on the diagnosis of ASD, particularly emphasizing the various types of datasets employed in deep learningbased methods. The study covers various datasets that include behavioral data, capturing intricate details of social interaction and communication patterns, neuroimaging data that provide insights into the structure and function of the brain, and datasets that incorporate genetic and clinical assessments. The paper provides a more in-depth analysis of the challenges faced while acquiring datasets, which include issues related to data reliability, the adequacy of sample sizes, and the variability present within the datasets
format Proceeding Paper
author Alam, Mohammad Shafiul
Rashid, Muhammad Mahbubur
Ali, Mohammad Yeakub
Yvette, Susiapan
author_facet Alam, Mohammad Shafiul
Rashid, Muhammad Mahbubur
Ali, Mohammad Yeakub
Yvette, Susiapan
author_sort Alam, Mohammad Shafiul
title Addressing the challenge of dataset acquisition for ASD diagnosis with deep learning-based neural networks
title_short Addressing the challenge of dataset acquisition for ASD diagnosis with deep learning-based neural networks
title_full Addressing the challenge of dataset acquisition for ASD diagnosis with deep learning-based neural networks
title_fullStr Addressing the challenge of dataset acquisition for ASD diagnosis with deep learning-based neural networks
title_full_unstemmed Addressing the challenge of dataset acquisition for ASD diagnosis with deep learning-based neural networks
title_sort addressing the challenge of dataset acquisition for asd diagnosis with deep learning-based neural networks
publisher AIP publishing
publishDate 2024
url http://irep.iium.edu.my/115389/7/115389_Addressing%20the%20challenge%20of%20dataset.pdf
http://irep.iium.edu.my/115389/8/115389_Addressing%20the%20challenge%20of%20dataset_Scopus.pdf
http://irep.iium.edu.my/115389/
https://pubs.aip.org/aip/acp/article-abstract/3161/1/020122/3310611/Addressing-the-challenge-of-dataset-acquisition?redirectedFrom=fulltext
https://doi.org/10.1063/5.0229866
_version_ 1814932541874896896