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A Deep Learning Method for Autism Spectrum Disorder

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Computational Intelligence in Machine Learning (ICCIML 2022)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1106))

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Abstract

The present study uses deep learning methods to detect autism spectrum disorder (ASD) in patients from global multi-site database Autism Brain Imaging Data Exchange (ABIDE) based on brain activity patterns. ASD is a neurological condition marked by repetitive behaviours and social difficulties. A deep learning-based approach using transfer learning for automatic detection of ASD is proposed in this study, which uses characteristics retrieved from the intracranial brain volume and corpus callosum from the ABIDE data set. T1-weighted MRI scans provide information on the intracranial brain volume and corpus callosum. ASD is detected using VGG-16 based on transfer learning.

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Author information

Authors and Affiliations

  1. Santhigiri College, Vazhithala, India

    Bindu George

  2. Department of Artificial Intelligence and Machine Learning, Coimbatore Institute of Technology, Coimbatore, India

    E. Chandra Blessie

  3. CHRIST(Deemed to be) University, Bengaluru, Karnataka, India

    K. R. Resmi

Authors
  1. Bindu George
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  2. E. Chandra Blessie
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  3. K. R. Resmi
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Corresponding author

Correspondence to K. R. Resmi .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, CMR Institute of Technology, Hyderabad, India

    Vinit Kumar Gunjan

  2. BioAxis DNA Research Centre, Hyderabad, India

    Amit Kumar

  3. Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

  4. Indian Institute of Technology Kanpur, Kanpur, India

    Sri Niwas Singh

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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George, B., Chandra Blessie, E., Resmi, K.R. (2024). A Deep Learning Method for Autism Spectrum Disorder. In: Gunjan, V.K., Kumar, A., Zurada, J.M., Singh, S.N. (eds) Computational Intelligence in Machine Learning. ICCIML 2022. Lecture Notes in Electrical Engineering, vol 1106. Springer, Singapore. https://doi.org/10.1007/978-981-99-7954-7_1

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