Deep learning-based feature selection for detection of autism spectrum disorder

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by challenges in communication, social interactions, and repetitive behaviors. The heterogeneity of symptoms across individuals complicates diagnosis. Neuroimaging techniques, particularly resting-state functional MRI (rs...

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Vydáno v:Frontiers in artificial intelligence Ročník 8; s. 1594372
Hlavní autoři: Nafisah, Ibrahim, Mahmoud, Nermine, Ewees, Ahmed A., Khattap, Mohamed G., Dahou, Abdelghani, Alghamdi, Safar M., Fares, Ibrahim A., Azmi Al-Betar, Mohammed, Abd Elaziz, Mohamed
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland Frontiers Media S.A 25.06.2025
Témata:
Artificial Intelligence
autism detection
deep learning
dynamic-opposite learning
feature selection
Hiking Optimization Algorithm
resting-state functional MRI (rs-fMRI)
deep learning
autism detection
resting-state functional MRI (rs-fMRI)
Hiking Optimization Algorithm
double attractors
dynamic-opposite learning
feature selection
ISSN:2624-8212, 2624-8212
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Shrnutí:Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by challenges in communication, social interactions, and repetitive behaviors. The heterogeneity of symptoms across individuals complicates diagnosis. Neuroimaging techniques, particularly resting-state functional MRI (rs-fMRI), have shown potential for identifying neural signatures of ASD, though challenges such as high dimensionality, noise, and small sample sizes hinder their clinical application. This study proposes a novel approach for ASD detection utilizing deep learning and advanced feature selection techniques. A hybrid model combining Stacked Sparse Denoising Autoencoder (SSDAE) and Multi-Layer Perceptron (MLP) is employed to extract relevant features from rs-fMRI data in the ABIDE I dataset, which was preprocessed using the CPAC pipeline. Feature selection is enhanced through an optimized Hiking Optimization Algorithm (HOA) that integrates DynamicOpposites Learning (DOL) and Double Attractors to improve convergence toward the optimal subset of features. The proposed model is evaluated using multiple ASD datasets. The performance metrics include an average accuracy of 0.735, sensitivity of 0.765, and specificity of 0.752, surpassing the results of existing state-of-the-art methods. The findings demonstrate the effectiveness of the hybrid deep learning approach for ASD detection. The enhanced feature selection process, coupled with the hybrid model, addresses limitations in current neuroimaging analyses and offers a promising direction for more accurate and clinically applicable ASD detection models.
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Judy Simon, SRM Arts and Science College, India
Edited by: Alaa Eleyan, American University of the Middle East, Kuwait
Reviewed by: Shijun Li, People's Liberation Army General Hospital, China
ISSN:2624-8212
2624-8212
DOI:10.3389/frai.2025.1594372