  In response to the global need for efficient early diagnosis of Autism
Spectrum Disorder (ASD), this paper bridges the gap between traditional,
time-consuming diagnostic methods and potential automated solutions. We propose
a multi-atlas deep ensemble network, MADE-for-ASD, that integrates multiple
atlases of the brain's functional magnetic resonance imaging (fMRI) data
through a weighted deep ensemble network. Our approach integrates demographic
information into the prediction workflow, which enhances ASD diagnosis
performance and offers a more holistic perspective on patient profiling. We
experiment with the well-known publicly available ABIDE (Autism Brain Imaging
Data Exchange) I dataset, consisting of resting state fMRI data from 17
different laboratories around the globe. Our proposed system achieves 75.20%
accuracy on the entire dataset and 96.40% on a specific subset $-$ both
surpassing reported ASD diagnosis accuracy in ABIDE I fMRI studies.
Specifically, our model improves by 4.4 percentage points over prior works on
the same amount of data. The model exhibits a sensitivity of 82.90% and a
specificity of 69.70% on the entire dataset, and 91.00% and 99.50%,
respectively, on the specific subset. We leverage the F-score to pinpoint the
top 10 ROI in ASD diagnosis, such as \emph{precuneus} and anterior
\emph{cingulate/ventromedial}. The proposed system can potentially pave the way
for more cost-effective, efficient and scalable strategies in ASD diagnosis.
Codes and evaluations are publicly available at TBA.
