Enhanced Early Autism Screening: Assessing Domain Adaptation with Distributed Facial Image Datasets and Deep Federated Learning - Shafiul Alam; Muhammad Mahbubur Rashid (International Islamic University Malaysia Engineering Journal)
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.