1
Higher Technological Institute, 10th of Ramadan City 44629, Egypt
(mona.fouad@hti.edu.eg )
2
Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt
(eng_samah2013@yahoo.com)
Abstract :
Asperger's syndrome, difficulties with disintegration in children, and autism are all included in the category of complex neurodevelopmental diseases known as autism spectrum disorders (ASD). Individuals who are autistic struggle greatly to keep up with society's speed, have poor communication skills, and struggle to express their emotions in the right ways. Early diagnosis and intervention can greatly improve the long-term outcomes for children with ASD. Several studies have identified key characteristics of autism using a variety of methods, including feature extraction, eye tracking, and speech recognition. As opposed to a person's emotional condition, facial recognition is more crucial in identifying autism. Early diagnosis and intervention can greatly improve the long-term outcomes for children with ASD. Hence, cutting-edge information technology that employs artificial intelligence (AI) techniques has assisted in the early diagnosis of ASD based on face pattern recognition. Among these techniques are deep learning (DL) have been utilized or suggested for detecting autism in youngsters. Herein, we applied a technique for accurate autism detection in children using facial analysis with the aid of computational intelligence. The proposed approach involves analyzing facial features and expressions to identify patterns which are associated with ASD. This is achieved by leveraging application of convolutional neural network (CNNs) to extract meaningful features from facial images. The extracted features are used to accurately classify children as either having or not having ASD. To evaluate the proposed approach, a dataset of facial images of children with and without ASD is used to train and validate the proposed technique. Also, to assess their performance in accurately detecting ASD. The proposed technique has the potential to revolutionize the way ASD is diagnosed by providing an objective and reliable tool for early detection and intervention.
Keywords :
Autism Detection; Computational Intelligence; Applied Intelligence; Facial Analysis; Deep Learning (DL); Convolutional Neural Network (CNNs).
References :
[1] T. Ghosh et al., “Artificial intelligence and internet of things in screening and management of autism spectrum disorder,” Sustain. Cities Soc., vol. 74, no. June, p. 103189, 2021, doi: 10.1016/j.scs.2021.103189.
[2] P. Anagnostopoulou, V. Alexandropoulou, G. Lorentzou, A. Lykothanasi, P. Ntaountaki, and A. Drigas, “Artificial intelligence in autism assessment,” Int. J. Emerg. Technol. Learn., vol. 15, no. 6, pp. 95–107, 2020, doi: 10.3991/IJET.V15I06.11231.
[3] E. S. Kim et al., “Social robots as embedded reinforcers of social behavior in children with autism,” J. Autism Dev. Disord., vol. 43, no. 5, pp. 1038–1049, 2013, doi: 10.1007/s10803-012-1645-2.
[4] F. W. Alsaade and M. S. Alzahrani, “Classification and Detection of Autism Spectrum Disorder Based on Deep Learning Algorithms,” Comput. Intell. Neurosci., vol. 2022, 2022, doi: 10.1155/2022/8709145.
[5] M. E. Król and M. Król, “A novel machine learning analysis of eye-tracking data reveals suboptimal visual information extraction from facial stimuli in individuals with autism,” Neuropsychologia, vol. 129, pp. 397–406, 2019.
[6] A. Postawka, “Behavior-Based Emotion Recognition Using Kinect and Hidden Markov Models,” in Artificial Intelligence and Soft Computing: 18th International Conference, ICAISC 2019, Zakopane, Poland, June 16–20, 2019, Proceedings, Part II 18, 2019, pp. 250–259.
[7] J. R. Quinlan, C4. 5: programs for machine learning. Elsevier, 2014.
[8] M. I. U. Haque and D. Valles, “A facial expression recognition approach using DCNN for autistic children to identify emotions,” in 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2018, pp. 546–551.
[9] A. T. Wieckowski, L. T. Flynn, J. A. Richey, D. Gracanin, and S. W. White, “Measuring change in facial emotion recognition in individuals with autism spectrum disorder: A systematic review,” Autism, vol. 24, no. 7, pp. 1607–1628, 2020.
[10] A. Pratap, C. S. Kanimozhiselvi, R. Vijayakumar, and K. V Pramod, “Soft computing models for the predictive grading of childhood Autism—a comparative study,” IJSCE, vol. 4, no. 3, pp. 64–67, 2014.
[11] G. Yolcu et al., “Deep learning-based facial expression recognition for monitoring neurological disorders,” in 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2017, pp. 1652–1657.
[12] O. Rudovic et al., “Culturenet: A deep learning approach for engagement intensity estimation from face images of children with autism,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018, pp. 339–346.
[13] Y. Tao and M.-L. Shyu, “SP-ASDNet: CNN-LSTM based ASD classification model using observer scanpaths,” in 2019 IEEE International conference on multimedia & expo workshops (ICMEW), 2019, pp. 641–646.
[14] M. Chita-Tegmark, “Social attention in ASD: A review and meta-analysis of eye-tracking studies,” Res. Dev. Disabil., vol. 48, pp. 79–93, 2016.
[15] M. Parvathi, “Early detection support mechanism in ASD using ML classifier,” Turkish J. Comput. Math. Educ., vol. 12, no. 10, pp. 4543–4549, 2021.
[16] S. L. Oh et al., “A novel automated autism spectrum disorder detection system,” Complex Intell. Syst., vol. 7, no. 5, pp. 2399–2413, 2021.
[17] V. Jagota, V. Bhatia, L. Vives, and A. B. Prasad, “ML-PASD: predict autism spectrum disorder by machine learning approach,” in Artificial Intelligence for Accurate Analysis and Detection of Autism Spectrum Disorder, IGI Global, 2021, pp. 82–93.
[18] R. Carette, F. Cilia, G. Dequen, J. Bosche, J.-L. Guerin, and L. Vandromme, “Automatic autism spectrum disorder detection thanks to eye-tracking and neural network-based approach,” in Internet of Things (IoT) Technologies for HealthCare: 4th International Conference, HealthyIoT 2017, Angers, France, October 24-25, 2017, Proceedings 4, 2018, pp. 75–81.
[19] T. G. Pratama, R. Hartanto, and N. A. Setiawan, “Machine learning algorithm for improving performance on 3 AQ-screening classification,” Commun. Sci. Technol., vol. 4, no. 2, pp. 44–49, 2019.
[20] S. R. Shahamiri and F. Thabtah, “Autism AI: a new autism screening system based on artificial intelligence,” Cognit. Comput., vol. 12, no. 4, pp. 766–777, 2020.
[21] F. Thabtah, F. Kamalov, and K. Rajab, “A new computational intelligence approach to detect autistic features for autism screening,” Int. J. Med. Inform., vol. 117, pp. 112–124, 2018.
[22] D. P. Wall, R. Dally, R. Luyster, J.-Y. Jung, and T. F. DeLuca, “Use of artificial intelligence to shorten the behavioral diagnosis of autism,” 2012.
[23] P. Washington et al., “Data-driven diagnostics and the potential of mobile artificial intelligence for digital therapeutic phenotyping in computational psychiatry,” Biol. Psychiatry Cogn. Neurosci. Neuroimaging, vol. 5, no. 8, pp. 759–769, 2020.
[24] A. J. Guimarães, V. J. S. Araujo, V. S. Araujo, L. O. Batista, and P. V de Campos Souza, “A hybrid model based on fuzzy rules to act on the diagnosed of autism in adults,” in Artificial Intelligence Applications and Innovations: 15th IFIP WG 12.5 International Conference, AIAI 2019, Hersonissos, Crete, Greece, May 24–26, 2019, Proceedings, 2019, pp. 401–412.
[25] M. Uddin, Y. Wang, and M. Woodbury-Smith, “Artificial intelligence for precision medicine in neurodevelopmental disorders,” NPJ Digit. Med., vol. 2, no. 1, p. 112, 2019.
[26] E. Askari, S. K. Setarehdan, A. Sheikhani, M. R. Mohammadi, and M. Teshnehlab, “Modeling the connections of brain regions in children with autism using cellular neural networks and electroencephalography analysis,” Artif. Intell. Med., vol. 89, pp. 40–50, 2018.
| Style | # |
|---|---|
| MLA | Mona Mohamed, Samah Ibrahim AbdelAal. "Auto-ASD Detector: Exploiting Computational Intelligence for autism spectrum disorders Detection in Children via Facial Analysis." Journal of Artificial Intelligence and Metaheuristics, Vol. 3, No. 1, 2023 ,PP. 42-50 (Doi : https://doi.org/10.54216/JAIM.030104) |
| APA | Mona Mohamed, Samah Ibrahim AbdelAal. (2023). Auto-ASD Detector: Exploiting Computational Intelligence for autism spectrum disorders Detection in Children via Facial Analysis. Journal of Journal of Artificial Intelligence and Metaheuristics, 3 ( 1 ), 42-50 (Doi : https://doi.org/10.54216/JAIM.030104) |
| Chicago | Mona Mohamed, Samah Ibrahim AbdelAal. "Auto-ASD Detector: Exploiting Computational Intelligence for autism spectrum disorders Detection in Children via Facial Analysis." Journal of Journal of Artificial Intelligence and Metaheuristics, 3 no. 1 (2023): 42-50 (Doi : https://doi.org/10.54216/JAIM.030104) |
| Harvard | Mona Mohamed, Samah Ibrahim AbdelAal. (2023). Auto-ASD Detector: Exploiting Computational Intelligence for autism spectrum disorders Detection in Children via Facial Analysis. Journal of Journal of Artificial Intelligence and Metaheuristics, 3 ( 1 ), 42-50 (Doi : https://doi.org/10.54216/JAIM.030104) |
| Vancouver | Mona Mohamed, Samah Ibrahim AbdelAal. Auto-ASD Detector: Exploiting Computational Intelligence for autism spectrum disorders Detection in Children via Facial Analysis. Journal of Journal of Artificial Intelligence and Metaheuristics, (2023); 3 ( 1 ): 42-50 (Doi : https://doi.org/10.54216/JAIM.030104) |
| IEEE | Mona Mohamed, Samah Ibrahim AbdelAal, Auto-ASD Detector: Exploiting Computational Intelligence for autism spectrum disorders Detection in Children via Facial Analysis, Journal of Journal of Artificial Intelligence and Metaheuristics, Vol. 3 , No. 1 , (2023) : 42-50 (Doi : https://doi.org/10.54216/JAIM.030104) |