AI-driven neuroanalytic modeling for mental health: multichannel CNN-based autism spectrum disorder detection via facial pattern analysis.

Introduction: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by abnormal brain connections, impaired cognitive functions, and dysfunctional behaviors, which, in mental health, is a major challenge to diagnose at an early age. Recent developments in Artificial Intelligence (AI) and computational neuroscience have made it possible to use neuroanalytic methods to identify subtle patterns related to brain disorders. Inspired by this, this study investigates facial pattern analysis as a non-invasive surrogate biomarker. Methods: A neuroanalytic deep-learning model is suggested on the basis of a Modified Histogram of Oriented Gradients-based Multichannel Convolutional Neural Network (MHMCNN). The technique comprises three steps, that is, (i) preprocessing and normalization of facial images, (ii) extraction of discriminative neuro-inspired features based on modified HOG descriptors, and (iii) multichannel CNN-based classification to discover complex structural and micro-pattern variations. The model is trained and tested on a publicly accessible facial autism dataset, and the performance of the model is tested using Results: The proposed MHMCNN framework achieved a validation accuracy of 98% and a test accuracy of 96.2%, demonstrating strong generalization capability for ASD facial image classification. The model attained a training accuracy of 99.8%, indicating effective feature learning during optimization. The combination of handcrafted feature descriptors and deep learning improves the feature representation and the strength of classification. Experimental findings support the enhanced generalization and stable recognition of ASD-related patterns. Discussion: The results emphasize the possible application of AI and computational neuroscience in neuroanalytic pattern detection in mental health diagnostics. The proposed solution offers a cost-effective and scalable solution to early screening of ASD by allowing observable facial characteristics to be related to underlying neurodevelopmental features. The work has helped in filling the gap between the phenotypic observations and the diagnosis of the disorder of the brain. Future studies will target the use of multimodal integration of neuroimaging and behavioral data to enhance understanding and clinical utility. Conclusion: This research introduces a new combination of AI and neuroanalytic principles to detect ASD that can further advance computational neuroscience-based mental health diagnostics. The suggested framework offers a scalable and affordable outcome of early screening and future expansion to multimodal frameworks of neuroimaging and behavioral data to increase clinical utility and interpretation.