Neuro-DANet: dual attention deep neural network long short term memory for autism spectrum disorder detection

Abstract

Autism spectrum disorder (ASD) is neurological illness affects ability of individuals to communicate and interact socially, and it is diagnosed in any time. Early detection of ASD is especially significant due to its subtle characteristics and high costs associated with the detection process. Traditional deep learning (DL) models struggle to capture intricate spatiotemporal dependencies in functional magnetic resonance imaging (fMRI) data, resulting in minimized detection performance and poor generalization. To address these drawbacks, the proposed Neuro-DANet combines a dual-attention deep neural network (DA-DNN) with long short term memory (LSTM) to efficiently learn spatial and temporal features from fMRI scans. The continuous wavelet transform (CWT) is used to extract multi-scale features and the principal component analysis (PCA) is utilized to dimensionality reduction, which enhances robustness and efficacy. The dual self-attention mechanism improves the interpretability of the model by focusing on critical brain regions and time steps that are most relevant to ASD severity. The developed Neuro-DANet obtains the highest accuracy of 98.51% on autism brain imaging data exchange (ABIDE)-I and 98.81% on ABIDE-II datasets when compared with traditional algorithms.