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Autism spectrum disorder detection using brain MRI image enabled deep learning with hybrid sewing training optimization

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Abstract

Autism spectrum disorder (ASD) is brain enabled disorder representing behaviors in a repetitive manner and social deficits. In this paper, ASD is diagnosed using brain magnetic resonance imaging (MRI) enabled deep learning with a hybrid optimization algorithm. Also, the hybrid optimization algorithm utilized is hybrid sewing training optimization (HSTO) which trains ZFNet for ASD detection. Pre-processing of the MRI image is done by Wiener filter and the filtered image is fed for region of interest extraction. Moreover, pivotal region extraction is carried out by the proposed HSTO, which is finally allowed for ASD detection by ZFNet. The proposed HSTO is formed by combining sewing training-based optimization and hybrid leader-based optimization. Furthermore, the performance of HSTO_ZFNet is found by five performance metrics of accuracy with 95.7%, true negative rate with 92.6%, true positive rate with 93.7%, false negative rate with 68.7%, and false positive rate with75.9%.

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Availability of data and materials

In case of benchmark data: Images are in Neuroimaging Informatics Technology Initiative (NIFTI) format and have been anonymised or defaced, at http://eprints.soton.ac.uk/id/eprint/448998.

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Funding

This research did not receive any specific funding.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Lendi Institute of Engineering and Technology, Jonnada Village, Vizianagaram District, Andhra Pradesh, 535005, India

    Vadamodula Prasad

  2. Department of CSE, Mohan Babu University, Sree Sainath Nagar, Tirupati, Andhra Pradesh, 517102, India

    G. V. Sriramakrishnan

  3. Department of CSE, School of Engineering and Technology, Christ University, Bengaluru, Karnataka, India

    I. Diana Jeba Jingle

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  1. Vadamodula Prasad
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  2. G. V. Sriramakrishnan
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  3. I. Diana Jeba Jingle
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Contributions

All authors have made substantial contributions to conception and design, revising the manuscript, and the final approval of the version to be published. Also, all authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Correspondence to Vadamodula Prasad.

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Prasad, V., Sriramakrishnan, G.V. & Diana Jeba Jingle, I. Autism spectrum disorder detection using brain MRI image enabled deep learning with hybrid sewing training optimization. SIViP 17, 4001–4008 (2023). https://doi.org/10.1007/s11760-023-02630-y

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  • DOI: https://doi.org/10.1007/s11760-023-02630-y

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