Hybrid weight optimized deep learning for autism and visual sensory processing disorder detection: A heuristic strategy on medical domain

Abstract

An Autism Spectrum Disorder (ASD) affected individual has several difficulties with social-emotional cues. The existing model is observed with emotional face processing in adolescents and ASD and Typical Development (TD) by utilizing various body parameters. Scanning facial expressions is the initial task, and recognizing the face’s sensitivity to different emotional expressions is the next complex task. To address this shortcoming, in this work, a new autism and visual Sensory Processing Disorder (SPD) detection model for supporting healthcare applications by processing facial expressions and sensory data of heart rate and body temperature. Here, initially, the individual data regarding facial emotions and other body parameters like heart rate and body temperature are collected from various subjects. Then, the selection of optimal features is executed by a hybrid algorithm named Density Factor-based Artificial Bee Honey Badger Optimization (DF-ABHBO), where the most essential features are attained and fed to the detection phase. The optimal feature selection is made by resolving the fitness function with constraints like correlation, data variance, and cosine similarity for inter and intra-class. Finally, the autism and visual SPD detection are performed through a Hybrid Weight Optimized Deep Neural Recurrent Network (HWODNRN), where the hyperparameter and weights of “Deep Neural Network (DNN) and Recurrent Neural Network (RNN)” are optimized with the developed DF-ABHBO technique. From the result analysis, the accuracy and F1-score rate of the offered DF-ABHBO-HWODNRN method have attained 96% and 93%. The findings obtained from the simulations of the designed system achieve better performance.