This study presents a comprehensive comparative analysis of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) in the realm of image segmentation tasks, with a particular emphasis on Neuroimaging Informatics Technology Initiative (NIfTI) images of individuals diagnosed with autism. By subjecting both models to rigorous training and evaluation processes, the research findings unveiled that the ViT network demonstrated superior accuracy when compared to CNNs specifically for the dataset under examination. This outcome underscores the ViT’s capability to potentially augment diagnostic precision in neuroimaging applications, offering promising avenues for further exploration in the field of medical imaging and clinical diagnosis.