Autism Spectrum Disorder detection framework for children based on federated learning integrated CNN-LSTM

Abdullah Lakhan; Mazin Abed Mohammed; Karrar Hameed Abdulkareem; Hassen Hamouda; Saleh Alyahya

doi:10.1016/j.compbiomed.2023.107539

Autism Spectrum Disorder detection framework for children based on federated learning integrated CNN-LSTM

Comput Biol Med. 2023 Oct 4:166:107539. doi: 10.1016/j.compbiomed.2023.107539. Online ahead of print.

Authors

Abdullah Lakhan¹, Mazin Abed Mohammed², Karrar Hameed Abdulkareem³, Hassen Hamouda⁴, Saleh Alyahya⁵

Affiliations

¹ Department of Cybersecurity and Computer Science, Dawood University of Engineering and Technology, Karachi City 74800, Sindh, Pakistan. Electronic address: abdullah.lakhan@duet.edu.pk.
² Department of Artificial Intelligence, College of Computer Science and Information Technology, University of Anbar, Anbar 31001, Iraq. Electronic address: mazinalshujeary@uoanbar.edu.iq.
³ College of Agriculture, Al-Muthanna University, Samawah 66001, Iraq. Electronic address: khak9784@mu.edu.iq.
⁴ College of Science and Humanities at Alghat, Majmaah University, Al-Majmaah 11952, Saudi Arabia. Electronic address: h.hamouda@mu.edu.sa.
⁵ Department of Electrical Engineering, College of Engineering and Information Technology, Onaizah Colleges, Onaizah 2053, Saudi Arabia. Electronic address: saleh.alyahya@oc.edu.sa.

PMID: 37804778
DOI: 10.1016/j.compbiomed.2023.107539

Abstract

The incidence of Autism Spectrum Disorder (ASD) among children, attributed to genetics and environmental factors, has been increasing daily. ASD is a non-curable neurodevelopmental disorder that affects children's communication, behavior, social interaction, and learning skills. While machine learning has been employed for ASD detection in children, existing ASD frameworks offer limited services to monitor and improve the health of ASD patients. This paper presents a complex and efficient ASD framework with comprehensive services to enhance the results of existing ASD frameworks. Our proposed approach is the Federated Learning-enabled CNN-LSTM (FCNN-LSTM) scheme, designed for ASD detection in children using multimodal datasets. The ASD framework is built in a distributed computing environment where different ASD laboratories are connected to the central hospital. The FCNN-LSTM scheme enables local laboratories to train and validate different datasets, including Ages and Stages Questionnaires (ASQ), Facial Communication and Symbolic Behavior Scales (CSBS) Dataset, Parents Evaluate Developmental Status (PEDS), Modified Checklist for Autism in Toddlers (M-CHAT), and Screening Tool for Autism in Toddlers and Children (STAT) datasets, on different computing laboratories. To ensure the security of patient data, we have implemented a security mechanism based on advanced standard encryption (AES) within the federated learning environment. This mechanism allows all laboratories to offload and download data securely. We integrate all trained datasets into the aggregated nodes and make the final decision for ASD patients based on the decision process tree. Additionally, we have designed various Internet of Things (IoT) applications to improve the efficiency of ASD patients and achieve more optimal learning results. Simulation results demonstrate that our proposed framework achieves an ASD detection accuracy of approximately 99% compared to all existing ASD frameworks.

Keywords: Autism Spectrum Disorder; CNN; Cloud; Federated learning; Fog; Healthcare; IoT; LSTM; Multimodal.