Skip to main content
Springer Nature Link
Log in

Significant CC400 Functional Brain Parcellations Based LeNet5 Convolutional Neural Network for Autism Spectrum Disorder Detection

  • Conference paper
  • First Online:
Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1704))

  • 588 Accesses

Abstract

Machine learning and computer vision have opened new pathways to investigate imaging data captured from different sensors. Numerous application areas are getting benefit from these advancements and one of these areas is medical imaging. Despite rapid advancements in machine learning based medical condition diagnosis systems (CADs), some ailments and disorders are hard to diagnose/classify due to the absence or the lack of consensus on biomarkers for specific disorders, like the Autism Spectrum Disorder (ASD). In this study, the challenging problem of classification of ASD using the magnetic resonance imaging (MRI) data is tackled. Hence, we propose an interpretable deep neural network based approach for ASD detection from MRI images. Our proposed explanation method is based on the selection of four regions of interest from the MRI images. The four significant CC400 functional brain parcellations are then concatenated and fed to a LeNet-5-based convolutional neural network to predict ASD. The performances of the proposed approach are evaluated on ABIDE dataset and promising results are achieved. Three augmented datasets are considered and an accuracy of \(95\%\) is achieved by using LeNet-5 which outperforms VGG16 and ResNet-50. The achieved accuracy outperforms also the existing deep neural networks based approaches on ABIDE dataset. The use of the four significant CC400 functional brain parcellations makes our approach more interpretable and more accurate.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://www.nmr.mgh.harvard.edu/lab/mripredict.

  2. 2.

    https://fcp-indi.github.io/docs/latest/user/quick.html#default-pipeline.

  3. 3.

    GridSearchCV.

References

  1. Li, H., Parikh, N.A., He, L.: A novel transfer learning approach to enhance deep neural network classification of brain functional connectomes. Front. Neurosci. 12, 491 (2018)

    Article  Google Scholar 

  2. Dekhil, O., et al.: Using resting state functional MRI to build a personalized autism diagnosis system. PloS ONE 13(10), e020635 (2018)

    Google Scholar 

  3. Sherkatghanad, Z., et al.: Automated detection of autism spectrum disorder using a convolutional neural network. Front. Neurosci. Annalen der Physik 13, 1325 (2020)

    Google Scholar 

  4. Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186 (2009)

    Article  Google Scholar 

  5. Yang, X., Schrader, P.T., Zhang, N.: A deep neural network study of the ABIDE repository on autism spectrum classification. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 11(4) (2020). https://doi.org/10.14569/IJACSA.2020.0110401

  6. Bellak, L.: The schizophrenic syndrome and attention deficit disorder: thesis, antithesis, and synthesis ? Am. Psychol. 49(1), 25 (1994)

    Article  Google Scholar 

  7. Just, M.A., Cherkassky, V.L., Buchweitz, A., Keller, T.A., Mitchell, T.M.: Identifying autism from neural representations of social interactions: neurocognitive markers of autism. PloS ONE 9(12), e113879 (2014)

    Google Scholar 

  8. Sabuncu, M.R., Konukoglu, E.: Clinical prediction from structural brain MRI scans: a large-scale empirical study. Neuroinformatics 13(1), 31–46 (2014). https://doi.org/10.1007/s12021-014-9238-1

    Article  Google Scholar 

  9. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (2013)

    MATH  Google Scholar 

  10. Konukoglu, E., Glocker, B., Zikic, D., Criminisi, A.: Neighbourhood approximation forests. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7512, pp. 75–82. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33454-2_10

    Chapter  Google Scholar 

  11. Tipping, M.E.: Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res. 1, 211–244 (2001)

    MathSciNet  MATH  Google Scholar 

  12. Sherkatghanad, Z., et al.: Automated detection of autism spectrum disorder using a convolutional neural network. Front. Neurosci. 13, 1325 (2020)

    Article  Google Scholar 

  13. Huang, Z.A., Zhu, Z., Yau, C.H., Tan, K.C.: Identifying autism spectrum disorder from resting-state fMRI using deep belief network. IEEE Trans. Neural Netw. Learn. Syst. 32, 847–2861 (2020)

    Google Scholar 

  14. Hinton, G.E.: Deep belief networks. Scholarpedia 4(5), 5947 (2009). Revision #91189

    Google Scholar 

  15. Thomas, R.M., Gallo, S., Cerliani, L., Zhutovsky, P., El-Gazzar, A., van Wingen, G.: Classifying autism spectrum disorder using the temporal statistics of resting-state functional MRI data with 3D convolutional neural networks. Front. Psychiatry 11, 440 (2020)

    Google Scholar 

  16. Dvornek, N.C., Ventola, P., Pelphrey, K.A., Duncan, J.S.: Identifying autism from resting-state fMRI using long short-term memory networks. In: Wang, Q., Shi, Y., Suk, H.-I., Suzuki, K. (eds.) MLMI 2017. LNCS, vol. 10541, pp. 362–370. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67389-9_42

    Chapter  Google Scholar 

  17. Di Martino, A., et al.: The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry 19(6), 659–667 (2014)

    Article  Google Scholar 

  18. Dvornek, N.C., Ventola, P., Duncan, J.S.: Combining phenotypic and resting-state fMRI data for autism classification with recurrent neural networks. In: Proceedings of the IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 725–728. IEEE (2018)

    Google Scholar 

  19. Eslami, T., Mirjalili, V., Fong, A., Laird, A.R., Saeed, F.: ASD-DiagNet: a hybrid learning approach for detection of autism spectrum disorder using fMRI data. Front. Neuroinform. 13, 70 (2019)

    Article  Google Scholar 

  20. Wang, Y., Wang, J., Wu, F.X., Hayrat, R., Liu, J.: AIMAFE: autism spectrum disorder identification with multi-atlas deep feature representation and ensemble learning. J. Neurosci. Methods 343, 108840 (2020)

    Article  Google Scholar 

  21. Craddock, C., et al.: The neuro bureau preprocessing initiative: open sharing of preprocessed neuroimaging data and derivatives. Front. Neuroinform. 7, 27 (2013)

    Google Scholar 

  22. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  23. Sarraf, S., Tofighi, G.: Classification of Alzheimer’s disease using fMRI data and deep learning convolutional neural networks. arXiv preprint arXiv:1603.08631 (2016)

  24. Wang, T., Lu, C., Shen, G., Hong, F.: Sleep apnea detection from a single-lead ECG signal with automatic feature-extraction through a modified LeNet-5 convolutional neural network. PeerJ 7, e7731 (2019)

    Article  Google Scholar 

  25. Zhang, S., et al.: Computer-aided diagnosis (CAD) of pulmonary nodule of thoracic CT image using transfer learning. J. Digit. Imaging 32(6), 995–1007 (2019)

    Article  Google Scholar 

  26. Walsh, J., Othmani, A., Jain, M., Dev, S.: Using U-net network for efficient brain tumor segmentation in MRI images. Healthc. Anal. 2, 100098 (2022). https://www.sciencedirect.com/science/article/pii/S2772442522000429

  27. Hu, W., Huang, Y., Wei, L., Zhang, F., Li, H.: Deep convolutional neural networks for hyperspectral image classification. J. Sens. 2015, 12 p., 258619 (2015). https://doi.org/10.1155/2015/258619

  28. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  29. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  30. Heinsfeld, A.S., Franco, A.R., Craddock, R.C., Buchweitz, A., Meneguzzi, F.: Identification of autism spectrum disorder using deep learning and the abide dataset. NeuroImage Clin. 17, 16–23 (2018)

    Article  Google Scholar 

  31. Huang, Z.A., Zhu, Z., Yau, C.H., Tan, K.C.: Identifying autism spectrum disorder from resting-state fMRI using deep belief network. IEEE Trans. Neural Netw. Learn. Syst. 32, 2847–2861 (2020)

    Article  Google Scholar 

  32. Ingalhalikar, M., Shinde, S., Karmarkar, A., Rajan, A., Rangaprakash, D., Deshpande, G.: Functional connectivity-based prediction of autism on site harmonized abide dataset. IEEE Trans. Biomed. Eng. 68(12), 3628–3637 (2021)

    Article  Google Scholar 

  33. Jha, R.R., Bhardwaj, A., Garg, D., Bhavsar, A., Nigam, A.: MHATC: autism spectrum disorder identification utilizing multi-head attention encoder along with temporal consolidation modules. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 337–341. IEEE (2022)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Université Paris-Est, LISSI, UPEC, 94400, Vitry sur Seine, France

    Alice Othmani

  2. Engineering School - EFREI Paris, 94800, Villejuif, France

    Thibaut Bizet, Tanguy Pellerin, Badr Hamdi & Marc-Antoine Bock

  3. School of Computer Science, University College Dublin, Dublin, Ireland

    Soumyabrata Dev

Authors
  1. Alice Othmani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thibaut Bizet
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tanguy Pellerin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Badr Hamdi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Marc-Antoine Bock
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Soumyabrata Dev
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alice Othmani .

Editor information

Editors and Affiliations

  1. University of South Dakota, Vermillion, SD, USA

    KC Santosh

  2. Texas A&M University, College Station, TX, USA

    Ayush Goyal

  3. University of Luxembourg, Luxembourg, Luxembourg

    Djamila Aouada

  4. University of Derby, Derby, UK

    Aaisha Makkar

  5. University of Minnesota, Minneapolis, MN, USA

    Yao-Yi Chiang

  6. IIIT Allahabad, Allahabad, India

    Satish K Singh

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Othmani, A., Bizet, T., Pellerin, T., Hamdi, B., Bock, MA., Dev, S. (2023). Significant CC400 Functional Brain Parcellations Based LeNet5 Convolutional Neural Network for Autism Spectrum Disorder Detection. In: Santosh, K., Goyal, A., Aouada, D., Makkar, A., Chiang, YY., Singh, S.K. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2022. Communications in Computer and Information Science, vol 1704. Springer, Cham. https://doi.org/10.1007/978-3-031-23599-3_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-23599-3_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23598-6

  • Online ISBN: 978-3-031-23599-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics