Utilizing Cutting-Edge Deep Learning Strategies and Harnessing the Power of a Pre-Trained ResNet18 Convolutional Neural Network for Assessing the Risk of Autism

In this study, the ResNet18 CNN classification method, a machine learning algorithm explored herein, is employed for the assessment of an individual's likelihood of developing autism spectrum disorder (ASD). The investigation utilizes a diverse dataset encompassing various factors, including behavioral traits, demographic information, and clinical markers associated with ASD. Leveraging the simplicity and effectiveness of the ResNet18 CNN technique in classification tasks, the study scrutinizes patterns within the dataset to make predictions regarding the probability of autism. The research initiates with the initial phase of data preparation, involving processes such as feature selection and normalization, aimed at optimizing the model's performance. Subsequently, a comprehensive evaluation of the ResNet18 CNN method is conducted, considering different hyperparameter configurations and assessing its effectiveness in accurately categorizing individuals into ASD and non-ASD groups. The results showcase the algorithm's efficacy in identifying subtle patterns within the dataset, thereby enhancing our understanding of the factors influencing the risk of autism. The implications of this study extend beyond diagnostic applications, as the precision of the model established here has the potential to assist clinicians and researchers in early identification and intervention for individuals at risk of ASD. This work contributes significantly to ongoing efforts focused on improving the accuracy and effectiveness of autism risk assessment through the integration of machine learning approaches. Ultimately, the goal of this research is to facilitate timely and targeted therapies, leading to improved outcomes in the realm of autism.