A Novel Machine Learning Based Framework for Detection of Autism Spectrum Disorder (ASD)

• Published in: Applied artificial intelligence Vol. 36; no. 1
• Main Authors: Sharif, Hamza, Khan, Rizwan Ahmed
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 31.12.2022; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Autism; Biomarkers; Brain; Classification; Deep learning; Domains; Feature extraction; Machine learning; Medical imaging
• ISSN: 0883-9514; 1087-6545
• DOI: 10.1080/08839514.2021.2004655

Autism Spectrum Disorder (ASD) is linked with abridged ability in social behavior. Scientists working in the broader domain of cognitive sciences have done a lot of research to discover the root cause of ASD, but its biomarkers are still unknown. Some studies from the domain of neuroscience have highlighted the fact that corpus callosum and intracranial brain volume hold significant information for the detection of ASD. Taking inspiration from such findings, in this article, we have proposed a machine learning based framework for automatic detection of ASD using features extracted from corpus callosum and intracranial brain volume. Our proposed framework has not only achieved good recognition accuracy but has also reduced the complexity of training machine learning model by selecting features that are most significant in terms of discriminative capabilities for classification of ASD. Second, for benchmarking and to verify potential of deep learning on analyzing neuroimaging data, in this article, we have presented results achieved by using the transfer learning approach. For this purpose, we have used the pre-trained VGG16 model for the classification of ASD.