  Mental disorders such as Autism Spectrum Disorders (ASD) are heterogeneous
disorders that are notoriously difficult to diagnose, especially in children.
The current psychiatric diagnostic process is based purely on the behavioural
observation of symptomology (DSM-5/ICD-10) and may be prone to over-prescribing
of drugs due to misdiagnosis. In order to move the field towards more
quantitative fashion, we need advanced and scalable machine learning
infrastructure that will allow us to identify reliable biomarkers of mental
health disorders. In this paper, we propose a framework called ASD-DiagNet for
classifying subjects with ASD from healthy subjects by using only fMRI data. We
designed and implemented a joint learning procedure using an autoencoder and a
single layer perceptron which results in improved quality of extracted features
and optimized parameters for the model. Further, we designed and implemented a
data augmentation strategy, based on linear interpolation on available feature
vectors, that allows us to produce synthetic datasets needed for training of
machine learning models. The proposed approach is evaluated on a public dataset
provided by Autism Brain Imaging Data Exchange including 1035 subjects coming
from 17 different brain imaging centers. Our machine learning model outperforms
other state of the art methods from 13 imaging centers with increase in
classification accuracy up to 20% with maximum accuracy of 80%. The machine
learning technique presented in this paper, in addition to yielding better
quality, gives enormous advantages in terms of execution time (40 minutes vs. 6
hours on other methods). The implemented code is available as GPL license on
GitHub portal of our lab (https://github.com/pcdslab/ASD-DiagNet).
