Comparison of different classification methods for autism spectrum diagnosis

Shumin Liu; Zhaohui Wang; LinMao Tian; YueFu Zhan

doi:10.1117/12.2644418

12 October 2022 Comparison of different classification methods for autism spectrum diagnosis

Shumin Liu, Zhaohui Wang, LinMao Tian, YueFu Zhan

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 123421R (2022) https://doi.org/10.1117/12.2644418
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

Studies have found autism spectrum disorder is a diffuse developmental disease of the central nervous system. The majority of autism cases result from a combination of genetic predisposition and environmental factors that influence early brain development, despite a few being caused by genes alone. Traditional diagnosis of autism spectrum disorder is usually through interviews and questionnaires, which takes plenty of time and might be misdiagnosed. The primary purpose of this study is to compare different classification methods for distinguishing autism spectrum disorder from typical development by machine learning and deep learning in recent years. The experiments are conducted to discuss their strengths and weaknesses, which, in turn, results are presented for further research.

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Shumin Liu, Zhaohui Wang, LinMao Tian, and YueFu Zhan "Comparison of different classification methods for autism spectrum diagnosis", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 123421R (12 October 2022); https://doi.org/10.1117/12.2644418

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KEYWORDS

Data modeling

Functional magnetic resonance imaging

Feature extraction

Neural networks

Machine learning

Brain

Electroencephalography

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