Game Theory Enhanced Deep Neuroimaging for Advanced Autism Spectrum Disorder Diagnosis
Keywords:
Autism Spectrum Disorder Diagnosis, Deep Fuzzy Neural Network, Feedback Henry Gas Optimization, Game Theory OptimizationAbstract
Autism spectrum disease (ASD) is a neuro-developmental disorder that is complicated and degenerative. The majority of current approaches use functional MRI to diagnose autism spectrum disorder (ASD), but they have several drawbacks. In order to address these issues, a novel framework for diagnosing ASD is presented by the suggested approach, which combines deep learning, sophisticated neuroimaging, and game theory. Using an optimized Deep Neuro Fuzzy Network (DNFN) through Feedback-Henry Gas Optimization (FHGO) and functional connectivity data, this study builds upon a novel approach and applies game theory to model the complex interactions within neural networks and improve the automated autism diagnosis model's performance. The proposed Game Theory Optimized DNFN-FHGO shows better accuracy and yield an accuracy of 98.63 % which is 17.54% higher when compared with DNN, SVM and DANN and establishing a new standard in the area by fusing the strategic insights of game theory, the adaptability of deep learning, and the predictive potential of neuroimaging.
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