Fuzzy Logic Based Deep Learning Approach (FRNN) for Autism Spectrum Disorder Detection

Autism Spectrum Disorder (ASD) is regarded as a spectrum of developmental problems with neurological roots that affect communication, social interaction, and behavior. As it has a detrimental effect on both psychological and physical health, the anxiety experienced by the average individual concerns the physician, especially for those with ASD. Researchers are working very hard to understand the genetic underpinnings of autism and use the genetic information to choose logical targets for efficient therapy. International consortiums are using linkage analysis to pinpoint chromosomes and their correlation with ASD in order to pinpoint potential genetic illnesses. This study applies a recurrent neural network with fuzzy logic and provides an empirical assessment of how well it can diagnose autism using data from an MRI scan of the brain. To evaluate their performances, precision, sensitivity, specificity, and classification accuracy metrics are used. The experimental results show that FRNN, a deep learning-based classifier, produces the best outcome.