Multiple Deep Learning Architectures Achieve Superior Performance Diagnosing Autism Spectrum Disorder Using Features Previously Extracted from Structural and Functional MRI.

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Abstract

The diagnosis of Autism Spectrum Disorder (ASD) is a subjective process requiring clinical expertise in neurodevelopmental disorders. Since such expertise is not available at many clinics, automated diagnosis using machine learning (ML) algorithms would be of great value to both clinicians and the imaging community to increase the diagnoses’ availability and reproducibility while reducing subjectivity. This research systematically compares the performance of classifiers using over 900 subjects from the IMPAC database [1], using the database’s derived anatomical and functional features to diagnose a subject as autistic or healthy. In total 12 classifiers are compared from 3 categories including: 6 nonlinear shallow ML models, 3 linear shallow models, and 3 deep learning models. When evaluated with an AUC ROC performance metric, results include: (1) amongst the shallow learning methods, linear models outperformed nonlinear models, agreeing with [2]. (2) Deep learning models outperformed shallow ML models. (3) The best model was a dense feedforward network, achieving 0.80 AUC which compares to the recently reported 0.79±0.01 AUC average of the top 10 methods from the IMPAC challenge [3]. These results demonstrate that even when using features derived from imaging data, deep learning methods can provide additional predictive accuracy over classical methods.