Abstract
Autism spectrum disorder (ASD) is a neuro-developmental disorder that affects 1% of children and has a lifetime effect on communication and interaction. Early prediction can address this problem by decreasing the severity. This paper presents a deep learning-based transfer learning applied to resting state fMRI images for predicting the autism disorder features. We worked with CNN and different transfer learning models such as Inception-V3, Resnet, Densenet, VGG16, and Mobilenet. We performed extensive experiments and provided a comparative study for different transfer learning models to predict the classification of ASD. Results demonstrated that VGG16 achieves high classification accuracy of 95.8% and outperforms the rest of the transfer learning models proposed in this paper and has an average improvement of 4.96% in terms of accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ali, M.T., Elnakieb, Y.A., Shalaby, A., Mahmoud, A., Switala, A., Ghazal, M., Khelifi, A., Fraiwan, L., Barnes, G., El-Baz, A.: Autism classification using smri: a recursive features selection based on sampling from multi-level high dimensional spaces. In: Proceedings of the IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 267–270 (2021)
American Psychiatric Association.: Diagnostic and Statistical Manual for Mental Disorders, 5th edn. https://doi.org/10.1176/appi.books.9780890425596 (2013)
Autism Speaks.: Autism and health: A special report. https://www.autismspeaks.org/autism-statistics-asd (2021)
Yang, X., Islam, M.S., Khaled, A.A.: Functional connectivity magnetic resonance imaging classification of autism spectrum disorder using the multisite abide dataset. In: Proceedings of the 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), pp. 1–4 (2019)
Di Martino, A., Mostofsky, S.: Autism brain imaging data exchange. fcon_1000.projects.nitrc.org/indi/abide/abide_I.html (2016)
Lai, M., Lee, J., Chiu, S., Charm, J., So, W.Y., Yuen, F.P., Kwok, C., Tsoi, J., Lin, Y., Zee, B.: A machine learning approach for retinal images analysis as an objective screening method for children with autism spectrum disorder. EClinicalMedicine 28. Article 100588 (2020)
Ang, C., Zheng, W., Zong, Y., Qiu, N., Lu, C., Zhang, X., Ke, X., Guan, C.: Automatic identification of high-risk autism spectrum disorder: a feasibility study using video and audio data under the still-face paradigm. IEEE Trans. Neural Syst. Rehabil. Eng. 28(11), 2401–2410 (2020)
Piana, S., Malagoli, C., Usai, M.C., Camurri, A.: Effects of computerized emotional training on children with high functioning autism. IEEE Trans. Affect. Comput. 12(4), 1045–1054 (2019)
Rusli, N., Sidek, S.N., Yusof, H.M., Ishak, N.I., Khalid, M., Dzulkarnain, A.A.A.: Implementation of wavelet analysis on thermal images for affective states recognition of children with autism spectrum disorder. IEEE Access 8, 120818–120834 (2020)
Islam, S., Akter, T., Zakir, S., Sabreen, S., Hossain, M.I.: Autism spectrum disorder detection in toddlers for early diagnosis using machine learning. In: Proceedings of the IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), pp. 1–6 (2020)
Akter, T., Khan, M.I., Ali, M.H., Satu, M.S., Uddin, M.J., Moni, M.A.: Improved machine learning based classification model for early autism detection. In: Proceedings of the 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), pp. 742–747 (2021)
Mujeeb Rahman, K.K., Subashini, M.M.: Identification of autism in children using static facial features and deep neural networks. Brain Sci. MDPI 12(1). Article 94 (2022)
Talkar, T., Williamson, J.R., Hannon, D.J., Rao, H.M., Yuditskaya, S., Claypool, K.T., Sturim, D., Nowinski, L., Saro, H., Stamm, C., Mody, M.: Assessment of speech and fine motor coordination in children with autism spectrum disorder. IEEE Access 8, 127535–127545 (2020)
Ke, F., Choi, S., Kang, Y.H., Cheon, K.A., Lee, S.W.: Exploring the structural and strategic bases of autism spectrum disorders with deep learning. IEEE Access 8, 153341–153352 (2020)
Sharif, H., Khan, R.A.: A novel machine learning based framework for detection of autism spectrum disorder (ASD). Appl. Artif. Intell. 36(1). Article 2004655 (2022)
Bosl, W.J., Tager-Flusberg, H., Nelson, C.A.: EEG analytics for early detection of autism spectrum disorder: a data-driven approach. Sci. Rep. 8(1), 1–20 (2018)
Lavanga, M., De Ridder, J., Kotulska, K., Moavero, R., Curatolo, P., Weschke, B., Riney, K., Feucht, M., Krsek, P., Nabbout, R., Jansen, A.C.: Results of quantitative EEG analysis are associated with autism spectrum disorder and development abnormalities in infants with tuberous sclerosis complex. Biomed. Sig. Process. Control 68, 102658 (2021)
Oh, S.L., Jahmunah, V., Arunkumar, N., Abdulhay, E.W., Gururajan, R., Adib, N., Ciaccio, E.J., Cheong, K.H., Acharya, U.R.: A novel automated autism spectrum disorder detection system. Complex Intell. Syst. 7(5), 2399–2413 (2021)
Weiss, K., Khoshgoftaar, T.M., Wang, D.: A survey of transfer learning. J. Big Data 3(1), 1–40 (2016)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818–2826 (2016)
Mascarenhas, S., Agarwal, M.: A comparison between vgg16, vgg19 and resnet50 architecture frameworks for image classification. In: Proceedings of the International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON) vol. 1, pp. 96–99 (2021)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint,arXiv:1409.1556 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Lakshmi, P.B., Reddy, V.D., Ghosh, S., Sengar, S.S. (2023). Classification of Autism Spectrum Disorder Based on Brain Image Data Using Deep Neural Networks. In: Bhateja, V., Yang, XS., Ferreira, M.C., Sengar, S.S., Travieso-Gonzalez, C.M. (eds) Evolution in Computational Intelligence. FICTA 2023. Smart Innovation, Systems and Technologies, vol 370. Springer, Singapore. https://doi.org/10.1007/978-981-99-6702-5_17
Download citation
DOI: https://doi.org/10.1007/978-981-99-6702-5_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-6701-8
Online ISBN: 978-981-99-6702-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)