Autism Detection Using Xception-based CNN with SVM on Resized and Normalized Neuroimaging Data

International Journal of Electronics and Communication Engineering

© 2024 by SSRG - IJECE Journal

Volume 11 Issue 12

Year of Publication : 2024

Authors : V. Hema, S. Gowri

10.14445/23488549/IJECE-V11I12P112

How to Cite?

V. Hema, S. Gowri, "Autism Detection Using Xception-based CNN with SVM on Resized and Normalized Neuroimaging Data," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 12, pp. 123-134, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I12P112

Abstract:

A complicated neurological disorder known as Autism Spectrum Disorder (ASD) causes a variety of signs and behaviors. For prompt assistance and treatment, a prompt and correct diagnosis of ASD is essential. The deep learning model built on the Xception platform paired with Support Vector Machines (SVM) using MRI data is a unique method for diagnosing and identifying ASD presented in this paper. The first step of the suggested technique is preparing neurological data, including magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI), through downsizing and normalisation to improve extracting features and lower computing costs. Following this, the Xception-based Convolutional Neural Network (CNN) automatically extracts basic features from brain imaging information. The CNN model performs exceptionally well at identifying elaborate trends and differentiating between those with ASD and those who are usually developing. SVM is applied to the retrieved features to improve accurate classification and the ability even more. This novel technique uses SVM's ability to discriminate and deep learning's capability in feature extraction. The suggested technique achieves outstanding accuracy, sensitivity, and specificity in ASD identification and identification, as demonstrated by research results on a large dataset. On scaled and normalized brain data, the combination of Xception-based CNN and SVM shows promise for accelerating early detection of ASD and enhancing knowledge of the neurological basis of this complex condition. This study lays the door for more accurate and obtainable tools for medical practitioners to help with the early detection and treatment of ASD, thereby improving the standard of life for those who are impacted by this disorder. The proposed Xception-CNN with SVM-Lagrangian Optimizer obtains a remarkable accuracy measurement of 95.13 %.

Keywords:

Autism Spectrum Detection, Xception Model, Convolutional Neural Network, Support Vector Machine, Normalization.

References:

[1] Dimitrios I. Zafeiriou et al., “Autism Spectrum Disorders: The Quest for Genetic Syndromes,” American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, vol. 162, no. 4, pp. 327-336, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Ian M. Shochet et al., “The Cooperative Research Centre for Living with Autism (Autism CRC) Conceptual Model to Promote Mental Health for Adolescents with ASD,” Clinical Child and Family Psychology Review, vol. 19, pp. 94-116, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Esraa Tarek Sadek et al., “Computer Vision Techniques for Autism Symptoms Detection and Recognition: A Survey,” International Journal of Intelligent Computing and Information Sciences, vol. 20, no. 2, pp. 89-111, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Amanda D. Heidgerken et al., “A Survey of Autism Knowledge in a Health Care Setting,” Journal of Autism and Developmental Disorders, vol. 35, no. 323-330, 2005.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Eleni Mitsea et al., “Metacognition, Mindfulness and Robots for Autism Inclusion,” International Journal of Recent Contributions from Engineering, Science & IT, vol. 8, no. 2, pp. 4-20, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Kristina Gasiewski et al., “Collaboration between Behavior Analysts and Occupational Therapists in Autism Service Provision: Bridging the Gap,” Behavior Analysis in Practice, vol. 14, pp. 1209-1222, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Fadi Thabtah, “Machine Learning in Autistic Spectrum Disorder Behavioral research: A Review and Ways Forward,” Informatics for Health and Social Care, vol. 44, no. 3, pp. 278-297, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Richard L. Moreland, John M. Levine, Socialization in Organizations and Work Groups, Psychology Press, pp. 1-44, 2001.
[Google Scholar] [Publisher Link]
[9] “Identifying Infants and Young Children With Developmental Disorders in the Medical Home: An Algorithm for Developmental Surveillance and Screening,” American Academy of Pediatrics, vol. 118, no. 1, pp. 405-420, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[10] David D. Schwartz et al., “Age-Related Differences in Prevalence of Autism Spectrum Disorder Symptoms in Children and Adolescents with Costello Syndrome,” American Journal of Medical Genetics Part A, vol. 173, no. 5, pp. 1294-1300, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Latha Soorya et al., “Prospective Investigation of Autism and Genotype-Phenotype Correlations in 22q13 Deletion Syndrome and SHANK3 Deficiency,” Molecular Autism, vol. 4, pp. 1-17, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Isabella R. Fernandes et al., “Genetic Variations on SETD5 underlying Autistic Conditions,” Developmental Neurobiology, vol. 78, no. 5, pp. 500-518, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Gary B. Mesibov, Victoria Shea, and Eric Schopler, The TEACCH Approach to Autism Spectrum Disorders, 1st ed., Springer New York, pp. 1-212, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Nina Scarpinato et al., “Caring for the Child With an Autism Spectrum Disorder in the Acute Care Setting,” Journal for Specialists in Pediatric Nursing, vol. 15, no. 3, pp. 244-254, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Suman Raj, and Sarfaraz Masood, “Analysis and Detection of Autism Spectrum Disorder Using Machine Learning Techniques,” Procedia Computer Science, vol. 167, pp. 994-1004, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Tania Akter et al., “Machine Learning-Based Models for Early Stage Detection of Autism Spectrum Disorders,” IEEE Access, vol. 7, pp. 166509-166527, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Ugur Erkan, and Dang N.H. Thanh, “Autism Spectrum Disorder Detection with Machine Learning Methods,” Current Psychiatry Research and Reviews Formerly: Current Psychiatry Reviews, vol. 15, no. 4, pp. 297-308, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Kayleigh K. Hyde et al., “Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: A Review,” Review Journal of Autism and Developmental Disorders, vol. 6, pp. 128-146, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Fawaz Waselallah Alsaade, and Mohammed Saeed Alzahrani, “Classification and Detection of Autism Spectrum Disorder Based on Deep Learning Algorithms,” Computational Intelligence and Neuroscience, vol. 2022, no. 1, pp. 1-11, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Mengyi Liao, Hengyao Duan, and Guangshuai Wang, “Application of Machine Learning Techniques to Detect the Children with Autism Spectrum Disorder,” Journal of Healthcare Engineering, vol. 2022, no. 1, pp. 1-10, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Ibrahim Abdulrab Ahmed et al., “Eye Tracking-Based Diagnosis and Early Detection of Autism Spectrum Disorder Using Machine Learning and Deep Learning Techniques,” Electronics, vol. 11, no. 4, pp. 1-27, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Miklos Kralik, Autism Spectrum Disorder Classification, 2020. [Online]. Available: https://www.kaggle.com/competitions/abide/data
[23] S.Gopal Krishna Patro, and Kishore Kumar Sahu, “Normalization: A Preprocessing Stage,” International Advanced Research Journal in Science, Engineering and Technology, vol. 2, no. 3, pp. vol. 2, no. 3, pp. 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[24] José M. Jerez et al., “Missing Data Imputation Using Statistical and Machine Learning Methods in a Real Breast Cancer Problem,” Artificial Intelligence in Medicine, vol. 50, no. 2, pp. 105-115, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Yuanyuan Liu et al., “Flower Classification via Convolutional Neural Network,” IEEE International Conference on Functional-Structural Plant Growth Modeling, Simulation, Visualization and Applications (FSPMA), Qingdao, China, pp. 110-116, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Giorgos Tolias, Ronan Sicre, and Hervé Jégou, “Particular Object Retrieval with Integral Max-Pooling of CNN Activations,” International Conference on Learning Representations, pp. 1-12, 2016.
[Google Scholar] [Publisher Link]
[27] Bernhard Scholkopf, Jean-Philippe Vert, and Koji Tsuda, Kernel Methods in Computational Biology, MIT Press, pp. 1-410, 2004.
[Google Scholar] [Publisher Link]
[28] M. Pal, and P.M. Mather, “Support Vector Machines for Classification in Remote Sensing,” International Journal of Remote Sensing, vol. 26, no. 5, pp. 1007-1011, 2005.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Muhammad Shuaib Qureshi et al., “Prediction and Analysis of Autism Spectrum Disorder Using Machine Learning Techniques,” Journal of Healthcare Engineering, vol. 2023, no. 1, pp. 1-11, 2023.
[CrossRef] [Google Scholar] [Publisher Link]