An architectural framework for automatic detection of autism using deep convolution networks and genetic algorithm

The brainchild in any medical image processing lied in how accurately the diseases are diagnosed. Especially in the case of neural disorders such as Autism Spectrum Disorder (ASD), accurate detection was still a challenge. Several noninvasive neuroimaging techniques provided experts information about the structure and function of the brain. As ASD was a neural disorder, MRI of the brain gave a complex structure and functionality. Many machine learning techniques were proposed to improve the classification and detection accuracy of autism in MRI images. Our work focused mainly on developing the architecture of CNN combining the Genetic Algorithm. Such AI techniques were very much needed for training as they gave better accuracy compared to traditional statistical methods