Enhanced Early Autism Screening: Assessing Domain Adaptation with Distributed Facial Image Datasets and Deep Federated Learning

Shafiul Alam; Muhammad Mahbubur Rashid

doi:10.31436/iiumej.v26i1.3186

Authors

Shafiul Alam International Islamic University Malaysia https://orcid.org/0009-0007-1653-8218
Muhammad Mahbubur Rashid International Islamic University Malaysia https://orcid.org/0000-0002-3520-0657

DOI:

https://doi.org/10.31436/iiumej.v26i1.3186

Keywords:

Artificail Intelligence, Deep learning, Autism Spectrum Disorder, Data Federation

Abstract

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.

ABSTRAK: Kajian ini menawarkan kemajuan yang ketara dalam bidang saringan awal autisme dengan menyediakan pelbagai set data imej wajah yang direka khusus untuk pengesanan Gangguan Spektrum Autisme (ASD). Kajian ini menonjol sebagai usaha perintis untuk menganalisis dua set data imej wajah – Kaggle dan YTUIA, menggunakan kaedah pembelajaran teragih untuk menyesuaikan perbezaan domain dengan jayanya. Skim pembelajaran teragih ini berkesan menangani isu integriti maklumat perubatan sensitif dan menjamin pembelajaran ciri yang meluas, yang membawa kepada prestasi penilaian yang lebih baik merentas set data yang berbeza. Dengan menggunakan Xception sebagai tunjang pembelajaran teragih, kadar ketepatan yang luar biasa hampir 90% dicapai merentas semua set ujian, mewakili peningkatan ketara lebih daripada 30% untuk set ujian domain yang berbeza. Hasil kerja ini merupakan sumbangan penting dan luar biasa dalam penyelidikan saringan awal autisme kerana set data yang unik dan baharu, kaedah analisis yang digunakan, serta tumpuan kepada kerahsiaan data. Sumber ini menawarkan pemahaman yang menyeluruh mengenai cabaran dan peluang dalam bidang diagnosis ASD, sesuai untuk para profesional dan sarjana yang berminat.

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