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

Nagashree Nagesh (Nagarjuna College of Engineering and Technology)
Premjyoti Patil (Nagarjuna College of Engineering and Technology)
Shantakumar Patil (Sai Vidya Institute of Technology)
Mallikarjun Kokatanur (Xebia IT Architects Pvt Ltd)

International Journal of Electrical and Computer Engineering
Vol 12, No 2: April 2022

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 functionality and anatomical structure of the brain. As autism is a neural disorder, magnetic resonance imaging (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 convolution neural networks (CNN) combining the genetic algorithm. Such artificial intelligence (AI) techniques were very much needed for training as they gave better accuracy compared to traditional statistical methods.