  Autism Spectrum Disorder is a condition characterized by a typical brain
development leading to impairments in social skills, communication abilities,
repetitive behaviors, and sensory processing. There have been many studies
combining brain MRI images with machine learning algorithms to achieve
objective diagnosis of autism, but the correlation between white matter and
autism has not been fully utilized. To address this gap, we develop a
computer-aided diagnostic model focusing on white matter regions in brain MRI
by employing radiomics and machine learning methods. This study introduced a
MultiUNet model for segmenting white matter, leveraging the UNet architecture
and utilizing manually segmented MRI images as the training data. Subsequently,
we extracted white matter features using the Pyradiomics toolkit and applied
different machine learning models such as Support Vector Machine, Random
Forest, Logistic Regression, and K-Nearest Neighbors to predict autism. The
prediction sets all exceeded 80% accuracy. Additionally, we employed
Convolutional Neural Network to analyze segmented white matter images,
achieving a prediction accuracy of 86.84%. Notably, Support Vector Machine
demonstrated the highest prediction accuracy at 89.47%. These findings not only
underscore the efficacy of the models but also establish a link between white
matter abnormalities and autism. Our study contributes to a comprehensive
evaluation of various diagnostic models for autism and introduces a
computer-aided diagnostic algorithm for early and objective autism diagnosis
based on MRI white matter regions.
