ResNeXt convolutional forward harmonic network for autism spectrum disorder detection in infant

The brain is an incredible example of biological engineering, managing human cognition, behavior, and perception. Autism Spectrum Disorder (ASD) emerges within this complex organ. It brings a variety of challenges and distinct traits that influence social interactions, communication, and sensory experiences. ASD is a developmental condition that appears in early childhood, impacting how individuals perceive and engage with their surroundings. Detecting ASD involves observing behavioral patterns, assessing communication skills, and recognizing specific behavioral traits. In this work, the ASD detection is done using the ResNeXt Convolutional Forward Harmonic Network (ResCFHN). At first, the input image is acquired from the specified dataset and is pre-processed using the Region of Interest (RoI) and Kuwahara filter techniques. Then, the extraction and identification of pivotal region is done using the Box Neighborhood Search Algorithm based on functional connectivity. Thereafter, the ResCFHN is used for performing ASD detection. The proposed ResCFHN is obtained by integrating ResNeXt with Convolutional Neural Network (CNN). The ResCFHN has achieved 91.094% of accuracy, with 90.774% of sensitivity and 92.879% of specificity.