Dual Deep Autoencoder Split Generative Adversarial Networks with Gooseneck Barnacle Optimization-Based Prediction of Autism Spectrum Disorder in Facial Images

Autism spectrum disorder (ASD) is a developmental disability that poses significant challenges in social interaction, communication, and behavior. Individuals with ASD have unique ways of interacting and communicating, and early prediction is crucial for timely therapy. Researchers are focusing on predicting ASD using image-processing techniques due to its neurological nature. The proposed novel Hybrid Convolutional Bilateral filter-based Deep Dual Swin Axial Generator Attention with Gooseneck Barnacle Optimization (FCB-DDSATGA-GBO) accurately predicts ASD. The facial image dataset is the input data source. The Hybrid Fast Convolutional Bilateral Filter (HFCBF) is used to pre-process the data. Dual Deep Autoencoder and Split Generative Adversarial Network (DDASGAN) is used to extract static features. Additionally, Swin-Gated Axial Attention Transformer (SGAAT) is used to segment the image. To forecast ASD, DDASGAN is used and optimized with Gooseneck Barnacle Optimization (GBO). The performance of the suggested methodology can be assessed using measures such as accuracy, recall, precision, sensitivity, f-score, and error, and compared to existing methods. The suggested FCB-DDSATGA-GBO model outperforms the current techniques, offering an enhanced f1-score of 99.66%, recall of 99.66%, accuracy of 99.67%, specificity of 99.67%, and precision of 99.66% when utilizing facial images.