| Title |
An Automatic Diagnostic Tool for Autism Spectrum Disorder using Structural Magnetic Resonance Imaging and a Tailored Binary Coded Genetic Algorithm |
| Authors |
(Vasily Sachnev);(Mahanand Belathur Suresh) |
| DOI |
https://doi.org/10.5573/IEIESPC.2024.13.3.236 |
| Keywords |
Autism spectrum disorder (ASD); MRI; Voxel based morphometry (VBM); Genetic algorithm; Extreme learning machine |
| Abstract |
In this paper, we propose an automatic diagnostic tool for autism based on machine learning and structural MRI. A set of 989 relevant features extracted from structural magnetic resonance imaging (MRI) present in the ABIDE database was used. The tool has two steps: searching for the best set of features using a tailored binary coded genetic algorithm and then using the selected set of features to train a classifier using an extreme learning machine. The trained classifier can efficiently identify autism spectrum disorder (ASD) versus healthy controls. The proposed tailored binary coded genetic algorithm uses a statistical selective crossover designed specifically for the classification problem, which significantly speeds up the genetic algorithm and improves the classification of ASD versus healthy controls. Extensive experiments with the proposed automatic autism diagnostic tool clearly indicate the advantages of the proposed method compared to previous approaches. |