Open Access

ARTICLE

Ensemble of Deep Learning with Crested Porcupine Optimizer Based Autism Spectrum Disorder Detection Using Facial Images

Jagadesh Balasubramani¹, Surendran Rajendran^1,*, Mohammad Zakariah², Abeer Alnuaim²

1 Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, Tamil Nadu, India
2 Department of Computer Science and Engineering, College of Applied Studies and Community Service, King Saud University, P.O. Box 22459, Riyadh, 11495, Saudi Arabia

* Corresponding Author: Surendran Rajendran. Email:

(This article belongs to the Special Issue: Advancements in Machine Learning and Artificial Intelligence for Pattern Detection and Predictive Analytics in Healthcare)

Computers, Materials & Continua 2025, 83(2), 2793-2807. https://doi.org/10.32604/cmc.2025.062266

Received 14 December 2024; Accepted 05 March 2025; Issue published 16 April 2025

Abstract

Autism spectrum disorder (ASD) is a multifaceted neurological developmental condition that manifests in several ways. Nearly all autistic children remain undiagnosed before the age of three. Developmental problems affecting face features are often associated with fundamental brain disorders. The facial evolution of newborns with ASD is quite different from that of typically developing children. Early recognition is very significant to aid families and parents in superstition and denial. Distinguishing facial features from typically developing children is an evident manner to detect children analyzed with ASD. Presently, artificial intelligence (AI) significantly contributes to the emerging computer-aided diagnosis (CAD) of autism and to the evolving interactive methods that aid in the treatment and reintegration of autistic patients. This study introduces an Ensemble of deep learning models based on the autism spectrum disorder detection in facial images (EDLM-ASDDFI) model. The overarching goal of the EDLM-ASDDFI model is to recognize the difference between facial images of individuals with ASD and normal controls. In the EDLM-ASDDFI method, the primary level of data pre-processing is involved by Gabor filtering (GF). Besides, the EDLM-ASDDFI technique applies the MobileNetV2 model to learn complex features from the pre-processed data. For the ASD detection process, the EDLM-ASDDFI method uses ensemble techniques for classification procedure that encompasses long short-term memory (LSTM), deep belief network (DBN), and hybrid kernel extreme learning machine (HKELM). Finally, the hyperparameter selection of the three deep learning (DL) models can be implemented by the design of the crested porcupine optimizer (CPO) technique. An extensive experiment was conducted to emphasize the improved ASD detection performance of the EDLM-ASDDFI method. The simulation outcomes indicated that the EDLM-ASDDFI technique highlighted betterment over other existing models in terms of numerous performance measures.

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