ASD-HybridNet: A hybrid deep learning framework for detection of autism spectrum disorder.
Authors
Rai N,Pradhan PC,Saikia H,Bhutia R,Singh OP
Affiliations (4)
Affiliations (4)
- Department of Electronics and Communication Engineering, Sikkim Manipal Institute of Technology, Majitar, Sikkim Manipal University, 737136, Sikkim, India.
- Department of Instrumentation Engineering, Assam Engineering College, Guwahati, 781013, Assam, India.
- Department of Mathematics, Sikkim University, Gangtok, 737102, Sikkim, India. Electronic address: [email protected].
- Department of AI&DS, Sikkim Manipal Institute of Technology, Majitar, Sikkim Manipal University, 737136, Sikkim, India.
Abstract
Current diagnostic methods for autism spectrum disorder (ASD) are based on subjective behavioral assessments, which present challenges to an accurate and early diagnosis. This paper proposes a hybrid deep learning framework, ASD-HybridNet, which integrates both region of interest (ROI) time series data and functional connectivity (FC) maps derived from functional magnetic resonance imaging (fMRI) data to improve ASD detection. Experiments on the ABIDE dataset demonstrate the effectiveness of the proposed method compared to existing approaches.
Topics
Journal Article