Integrating Convolutional Neural Networks (CNN) and Machine Learning for Accurate Identification of Autism Spectrum Disorder Using Facial Biomarkers

The work presents a novel approach to address the challenging task of accurately identifying Autism Spectrum Disorder (ASD) by Incorporating Convolutional Neural Networks (CNN) into conventional machine learning methodologies, with a specific focus on leveraging facial biomarkers. Facial biomarkers offer a non-invasive avenue for ASD detection, relying on subtle expressions and features as early indicators. The intricate nature of these biomarkers necessitates advanced analytical tools, prompting the fusion of CNN, known for its prowess in image analysis, with traditional machine learning for enhanced accuracy.Our research emphasizes the potential of this integrated approach to advance ASD identification methodologies. Swift recognition is pivotal for prompt intervention, significantly shaping the trajectory of individuals navigating the autism spectrum. By concentrating on facial biomarkers and combining the strengths of deep learning with established machine learning principles, our methodology aims to provide a nuanced and precise understanding of ASD-related patterns.We employ a meticulously curated dataset from Kaggle, comprising images representing both ASD and non-ASD cases in children. The study involves training, validation, and testing phases, incorporating a CNN model followed by a comparative analysis with a traditional machine learning model. The obtained results not only illuminate the effectiveness of our proposed integrated approach but also showcase potential applications in the realm of neurological disorder diagnostics.