Convolutional Neural Network for Prediction of Autism based on Eye-tracking Scanpaths
DOI:
https://doi.org/10.61841/kpqpc028Keywords:
Autism Spectrum Disorder (ASD), Typical Developing (TD), Eye Tracking, Scanpath, Convolutional Neural Network (CNN)Abstract
Autism Spectrum Disorder (ASD), difficulty in socialization, can be detected by observation of atypical visual attention of children. Eye tracking is one of the most important techniques used in providing information on visual behavior as a statistically motivated step towards the accurate diagnosis of such disorders. The scanpath, sequences of fixations of the eyes on an image, provides data related to the locations and durations of the gazes that can be used to develop visual patterns to analyze the visual behavior of children. The aim of this paper is to develop a deep learning model implementing a convolutional neural network (CNN) to classify children as autistic or typically developing according to eye-tracking scanpaths. The model was applied to 29 autistic and 30 normal children and achieved 98% testing accuracy.
Downloads
References
[1] Zeyad Abdulhameed Taha Ahmed and Mukti E. Jadhav , A Review of Early Detection of Autism Based on Eye-Tracking and Sensing Technology, 5th International Conference on Inventive Computation Technologies (ICICT-2020), INDIA, IEEE. “Under Publication”
[2] Carette, R., Elbattah, M., Dequen, G., Guérin, J., Cilia, F., & Bosche, J. (2019). Learning to predict autism spectrum disorder based on the visual patterns of eye-tracking scanpaths. In Proceedings of the 12th International Conference on Health Informatics.
[3] https://figshare.com/articles/Visualization_of_EyeTracking_Scanpaths_in_Autism_Spectrum_Disorder_Image_Dataset/7073087/1 age_Dataset/7073087/1
[4] Elbattah, M., Carette, R., Dequen, G., Guérin, J. L., & Cilia, F. (2019, July). Learning Clusters in Autism Spectrum Disorder: Image-Based Clustering of Eye-Tracking Scanpaths with Deep Autoencoder. In 2019
41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1417-1420). IEEE.
[5] Goldberg, J. H., & Helfman, J. I. (2010, March). Visual scanpath representation. In Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications (pp. 203-210).
[6] Rutherford, M. D., & Towns, A. M. (2008). Scan path differences and similarities during emotion perception in those with and without autism spectrum disorders. Journal of Autism and Developmental Disorders, 38(7), 1371-1381.
[7] Jiang, M., & Zhao, Q. (2017). Learning visual attention to identify people with autism spectrum disorder. In Proceedings of the IEEE International Conference on Computer Vision (pp. 3267-3276).
[8] Wu, C., Liaqat, S., Cheung, S. C., Chuah, C. N., & Ozonoff, S. (2019, July). Predicting Autism Diagnosis using Images with Fixations and Synthetic Saccade Patterns. In 2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) (pp. 647-650). IEEE.
[9] Tao, Y., & Shyu, M. L. (2019, July). SP-ASDNet: CNN-LSTM-Based ASD Classification Model Using Observer Scanpaths. In 2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) (pp. 641-646). IEEE
[10] Xie, J., Wang, L., Webster, P., Yao, Y., Sun, J., Wang, S., & Zhou, H. (2019). A Two-Stream End-to-End Deep Learning Network for Recognizing Atypical Visual Attention in Autism Spectrum Disorder. arXiv preprint arXiv:1911.11393.
[11] Mohamed Elgendy, (2019) Deep Learning for Vision Systems, available online at https://www.manning.com.
Downloads
Published
Issue
Section
License
Copyright (c) 2020 AUTHOR
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
