RESUMEN
Autism spectrum disorder (ASD) is an umbrella term for a number of neurodevelopmental conditions with many heterogeneous behavioural indications. Recent medical imaging approaches use functional Magnetic Resonance Imaging (fMRI) for human recognition of the various neurological syndromes. However, these traditional techniques are time consuming and expensive. Thus, in this research, an optimization assisted deep learning technique, named Feedback Artificial Virus Optimization (FAVO)-based deep residual network (DRN), is developed. FAVO-based DRN is designed to incorporate the Feedback Artificial Tree (FAT) algorithm with Anti Corona Virus Optimization (ACVO). First, Region-Of-Interest extraction is carried out using thresholding techniques with nub region extraction completed using the proposed FAVO algorithm. ASD classification is then carried out using a DRN classifier. Evaluation of the proposal uses the ABIDE-1 and ABIDE-2 datasets. The developed FAVO algorithm attains better accuracy, sensitivity, and specificity of 0.9214, 0.9365, and 0.9142, respectively, by considering ABIDE-2 dataset. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
RESUMEN
Autism spectrum disorder (ASD) is an umbrella term for a number of neurodevelopmental conditions with many heterogeneous behavioural indications. Recent medical imaging approaches use functional Magnetic Resonance Imaging (fMRI) for human recognition of the various neurological syndromes. However, these traditional techniques are time consuming and expensive. Thus, in this research, an optimization assisted deep learning technique, named Feedback Artificial Virus Optimization (FAVO)‐based deep residual network (DRN), is developed. FAVO‐based DRN is designed to incorporate the Feedback Artificial Tree (FAT) algorithm with Anti Corona Virus Optimization (ACVO). First, Region‐Of‐Interest extraction is carried out using thresholding techniques with nub region extraction completed using the proposed FAVO algorithm. ASD classification is then carried out using a DRN classifier. Evaluation of the proposal uses the ABIDE‐1 and ABIDE‐2 datasets. The developed FAVO algorithm attains better accuracy, sensitivity, and specificity of 0.9214, 0.9365, and 0.9142, respectively, by considering ABIDE‐2 dataset. [ FROM AUTHOR] Copyright of Expert Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)