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...
Saved in:
Main Authors: | , , , |
---|---|
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 |