A novel framework for automatic detection of Autism: A study on Corpus
Callosum and Intracranial Brain Volume
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by
Hamza Sharif, Rizwan Ahmed Khan
2019
Abstract
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, due to intense connectivity of neurological pattern in brain.
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 applied machine learning algorithms on features extracted from corpus
callosum and intracranial brain volume data. Corpus callosum and intracranial
brain volume data is obtained from s-MRI (structural Magnetic Resonance
Imaging) data-set known as ABIDE (Autism Brain Imaging Data Exchange). Our
proposed framework for automatic detection of ASD showed potential of machine
learning algorithms for development of neuroimaging data understanding and
detection of ASD. Proposed framework enhanced achieved accuracy by calculating
weights / importance of features extracted from corpus callosum and
intracranial brain volume data.
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