  Computer vision and machine learning are the linchpin of field of automation.
The medicine industry has adopted numerous methods to discover the root causes
of many diseases in order to automate detection process. But, the biomarkers of
Autism Spectrum Disorder (ASD) are still unknown, let alone automating its
detection. Studies from the neuroscience domain highlighted the fact that
corpus callosum and intracranial brain volume holds significant information for
detection of ASD. Such results and studies are not tested and verified by
scientists working in the domain of computer vision / machine learning. Thus,
in this study we have proposed a machine learning based framework for automatic
detection of ASD using features extracted from corpus callosum and intracranial
brain volume from ABIDE dataset. Corpus callosum and intracranial brain volume
data is obtained from T1-weighted MRI scans. Our proposed framework first
calculates weights of features extracted from Corpus callosum and intracranial
brain volume data. This step ensures to utilize discriminative capabilities of
only those features that will help in robust recognition of ASD. Then,
conventional machine learning algorithm (conventional refers to algorithms
other than deep learning) is applied on features that are most significant in
terms of discriminative capabilities for recognition of ASD. Finally, for
benchmarking and to verify potential of deep learning on analyzing neuroimaging
data i.e. T1-weighted MRI scans, we have done experiment with state of the art
deep learning architecture i.e. VGG16 . We have used transfer learning approach
to use already trained VGG16 model for detection of ASD. This is done to help
readers understand benefits and bottlenecks of using deep learning approach for
analyzing neuroimaging data which is difficult to record in large enough
quantity for deep learning.
