Autism Screening using Deep Learning
Article Sidebar
Main Article Content
Abstract
Autism spectrum disorder (ASD) is growing among childrens. Autism is normal chronic disorder which fails a person or a child to interact with others socially. Detecting of autism through screening test is very time consuming and cost effective till now there is no cure for autism. But if it is detected earlier we canmotivate themto move socially and decrease the level of autism in them. So we detect autism using deep learning techniques so that we can detect it earlier stages.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
How to Cite
References
Arthi, K &Tamilarasi, A 2009, ‘A hybrid model in prediction of adhd using artificial neural networks’, International Journal of Information Technology and Knowledge Management, vol. 2, no. 1, pp. 209-215.
Florio, T, Einfeld, S, Tonge, B & Brereton, A 2009, ‘Providing an independent second opinion for the diagnosis of autism using artificial intelligence over the internet’, Counselling, Psychotherapy and Health: The use of Technology in Mental Health, vol. 5, no. 1, pp. 232-248.
Arthi, K, Tamilarasi, A &Papageorgiou, EI 2011, ‘Analyzing the performance of fuzzy cognitive maps with non-linear hebbian learning algorithm in predicting autistic disorder’, Expert Systems with Applications, vol. 38, no. 3, pp.1282-1292 https://doi.org/10.1016/j.eswa.2010.06.069
Guillén, R, Jensen, C & Edelson, S 2010, ‘A machine learning approach for identifying subtypes of autism’, In Proceedings of the 1st ACM International Health Informatics Symposium, pp. 620-628. ACM. https://doi.org/10.1145/1882992.1883091
Veeraraghavan, S & Srinivasan, K 2007, ‘Exploration of autism expert systems’, Fourth International Conference on Information Technology ITNG’07, pp.261–264, IEEE. https://doi.org/10.1109/ITNG.2007.91
van den Bekerom B. Using machine learning for detection of autism spectrum disorder. 2017.
Wall DP, Kosmicki J, Deluca TF, Harstad E, Fusaro VA, Use of machine learning to shorten observation-based screening and diagnosis of autism. Translational psychiatry. 2012;2:e100. https://doi.org/10.1038/tp.2012.10
Heinsfeld AS, Franco AR, Craddock RC, Buchweitz A, Meneguzzi F. Identification of autism spectrum disorder using deep learning and the ABIDE dataset. NeuroImage: Clinical. 2018 Jan 1;17:16-23. https://doi.org/10.1016/j.nicl.2017.08.017
Golestan, Shadan, PegahSoleiman, and Hadi Moradi. "A Comprehensive Review of Technologies Used for Screening, Assessment, and Rehabilitation of Autism Spectrum Disorder." arXiv preprint arXiv:1807.10986 (2018).
Muhammad Nazrul Islam, Kazi Shahrukh Omar, Prodipta Mondal, Nabila Shahnaz Khan, “A Machine Learning Approach to Predict Autism Spectrum Disorder”,2019 International Conference on Electrical, Computer and Communication Engineering (ECCE)2019 978-1-5386-9111-3/19 ©2019 IEEE.
Mahmoud Elbattah, Romuald Carette, Gilles Dequen, Jean-Luc Guérin, Federica Cilia,” Learning Clusters in Autism Spectrum Disorder: Image-Based Clustering of Eye-Tracking Scanpaths with Deep Autoencoder”, 978-1-5386-1311-5/19 ©2019 IEEE.
Thabtah, F. (2018). Machine learning in autistic spectrum disorder behavioral research: A review. To appear in Informatics for Health and Social Care Journal. https://doi.org/10.1080/17538157.2017.1399132
Alanoud Bin Dris, AbdulmalikAlsalman, Areej Al-Wabil, Mohammed Aldosari, Centre for Cyber Security Technology, King Abdulaziz City for Science and Technology (KACST) Riyadh, Saudi Arabia, 2019 IEEE
Fatiha Nur Büyükoflaz, Ali Öztürk, “Early Autism Diagnosis of Children with Machine Learning Algorithms,” BilgisayarMühendisli˘giBölümü, KTO KaratayÜniversitesi Konya, Türkiye, 2018 IEEE
Dingan Liao, Hu Lu, “Classify Autism and Control Based on Deep Learning and Community Structure on Resting-state fMRI”, IEEE Tenth International Conference on Advanced Computational Intelligence, (2018) March, pp.289-294.
Anibal SólonHeinsfeld, Alexandre Rosa Franco, R. Cameron Craddock, Augusto Buchweitz, Felipe Meneguzzi, “Identification of ASD using deep learning and the ABIDE dataset”, Elsevier Neuroimage Clinical (17), (2018), pp.16-23. https://doi.org/10.1016/j.nicl.2017.08.017
Yan Jin, Chong-Yaw Wee, Feng Shi, Kim-Han Thung, Dong Ni, Pew-Thian Yap, Dinggang Shen, “Identification of Infants at High-Risk for ASD Using Multiparameter Multiscale White Matter Connectivity Networks”, HHS Public Access, (2016) January, pp. 4880–4896. https://doi.org/10.1002/hbm.22957
M Duda, R Ma, N Haber, DP Wall, “Use of machine learning for behavioural distinction of autism and ADHD”,Transl Psychiatry (2016) 6, e732; doi:10.1038/tp.2015.221 https://doi.org/10.1038/tp.2015.221
BeykikhoshkAdham, Ognjen Arandjelovic, Svetha Venkatesh, and Dinh Phung. Hierarchical dirichlet process for tracking complex topical structure evolution and its application to autism research literature. In Advances in Knowledge Discovery and Data Mining - 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part I, pages 550–562, 2015. https://doi.org/10.1007/978-3-319-18038-0_43
DP Wall, J Kosmicki, TF Deluca, E Harstad, and VA Fusaro. Use of machine learning to shorten observation-based screening and diagnosis of autism. Translational Psychiatry, 2(4):e100, 2012. https://doi.org/10.1038/tp.2012.10
Behera, D. K., Das, M., & Swetanisha, S. (2019). A Research on Collaborative Filtering Based Movie Recommendations: From Neighborhood to Deep Learning Based System. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 4, pp. 10809–10814). https://doi.org/10.35940/ijrte.d4362.118419
Wanjau, S. K., Wambugu, G. M., & Oirere, A. M. (2022). Network Intrusion Detection Systems: A Systematic Literature Review o f Hybrid Deep Learning Approaches. In International Journal of Emerging Science and Engineering (Vol. 10, Issue 7, pp. 1–16). https://doi.org/10.35940/ijese.f2530.0610722
Nikam, S. S., & Dalvi, Prof. R. (2020). Fake News Detection on social media using Machine Learning Techniques. In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 7, pp. 940–943). https://doi.org/10.35940/ijitee.g5428.059720
Mulla, A. R., & Patil, S. S. (2019). Micro Clustering Methodology for Document Objects using Deep Learning Techniques. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 2, pp. 1546–1552). https://doi.org/10.35940/ijeat.b3716.129219
Alam, S. S., Mallick, S., & Habib, A.-Z. S. B. (2021). Performance Analysis of Cognitive Radios in a Cooperative Scheme over Nakagami Fading Channels. In Indian Journal of Signal Processing (Vol. 1, Issue 3, pp. 8–14). https://doi.org/10.54105/ijsp.c1009.081321