Enhanced early autism screening: assessing domain adaptation with distributed facial image datasets and deep federated learning
This study offers a significant advancement in the area of early autism screening by offering diverse domain facial image datasets specifically designed for the detection of Autism Spectrum Disorder (ASD). It stands out as the pioneering effort to analyze two facial image datasets – Kaggle and YT...
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| Main Authors: | , |
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| Format: | Article |
| Language: | en en |
| Published: |
2025
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/118565/13/118565_%20Enhanced%20early%20autism%20screening.pdf http://irep.iium.edu.my/118565/19/118565_Enhanced%20early%20autism%20screening_Scopus.pdf http://irep.iium.edu.my/118565/ https://doi.org/10.31436/iiumej.v26i1.3186 |
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| Summary: | This study offers a significant advancement in the area of early autism screening
by offering diverse domain facial image datasets specifically designed for the detection of
Autism Spectrum Disorder (ASD). It stands out as the pioneering effort to analyze two facial
image datasets – Kaggle and YTUIA, using federated learning methods to adapt domain
differences successfully. The federated learning scheme effectively addresses the integrity
issue of sensitive medical information and guarantees a wide range of feature learning, leading
to improved assessment performance across diverse datasets. By employing Xception as the
backbone for federated learning, a remarkable accuracy rate of almost 90% is attained across
all test sets, representing a significant enhancement of more than 30% for the different domain
test sets. This work is a significant and remarkable contribution to early autism screening
research due to its unique novel dataset, analytical methods, and focus on data confidentiality.
This resource offers a comprehensive understanding of the challenges and opportunities in
the field of ASD diagnosis, catering to both professionals and aspiring scholars. |
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