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A Deep Concatenated Convolutional Neural Network-Based Method to Classify Autism

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Neural Information Processing (ICONIP 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1794))

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Abstract

Associating different brain regions relating to a particular neurological issue has emerged as an area of neuroimaging research. Deep learning algorithms have emerged as a promising approach to automate neural data processing for classifying traits or characteristics associated with a range of conditions. The present paper has worked on improving binary-classification accuracy of Autism Spectrum Disorder (ASD) by distinguishing ASD from Typically Developing (TD) individuals. A hybrid model is proposed concatenating VGGNet and ResNet-152 to fuse the most discerning heterogeneous features from both networks to build a strong feature vector for attaining high classification accuracy. The effectiveness of the proposed approach is demonstrated on ABIDE dataset, which showed an improvement over state-of-art classifiers in terms of accuracy (88.12%), sensitivity (91.32%), specificity (86.34%) and ROC (0.88), respectively, in classifying ASD and TD individuals.

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Acknowledgement

This work is supported by the AI-TOP (2020-1-UK01-KA201-079167) projects funded by the European Commission under the Erasmus+ programme.

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Authors and Affiliations

  1. Indian Institute of Information Technology Una, Una, Himachal Pradesh, India

    Tanu Wadhera

  2. Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK

    Mufti Mahmud & David J. Brown

  3. Computing and Informatics Research Centre, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK

    Mufti Mahmud & David J. Brown

  4. Medical Technologies Innovation Facility, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK

    Mufti Mahmud & David J. Brown

Authors
  1. Tanu Wadhera
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  2. Mufti Mahmud
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  3. David J. Brown
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Correspondence to Mufti Mahmud .

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Editors and Affiliations

  1. Indian Institute of Technology Indore, Indore, India

    Mohammad Tanveer

  2. Indian Institute of Information Technology - Allahabad, Prayagraj, India

    Sonali Agarwal

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

  4. Indian Institute of Technology Patna, Patna, India

    Asif Ekbal

  5. University of Innsbruck, Innsbruck, Austria

    Adam Jatowt

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Wadhera, T., Mahmud, M., Brown, D.J. (2023). A Deep Concatenated Convolutional Neural Network-Based Method to Classify Autism. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1794. Springer, Singapore. https://doi.org/10.1007/978-981-99-1648-1_37

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  • DOI: https://doi.org/10.1007/978-981-99-1648-1_37

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