Unveiling Autism: Machine Learning-based Autism Spectrum Disorder Detection through MRI Analysis

PDF (1293KB), PP.10-23

Views: 0 Downloads: 0

Author(s)

Chitta Hrudaya Neeharika 1,* Yeklur Mohammed Riyazuddin 1

1. Department of Computer Science and Engineering, GITAM University, Hyderabad, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2025.02.02

Received: 19 Sep. 2024 / Revised: 17 Dec. 2024 / Accepted: 25 Jan. 2025 / Published: 8 Apr. 2025

Index Terms

Autism Spectrum Disorder, Brain Images, Machine Learning, Classifier Model

Abstract

The prediction of autism features in relation to age groups has not been definitively addressed, despite the fact that several studies have been conducted using various methodologies. Research in the field of neuroscience has demonstrated that intracranial brain volume and the corpus callosum provide crucial information for the identification of autism spectrum disorder (ASD). Based on these findings, we present Decision Tree-based Autism Prediction System (DT-APS) and Random Forest-based Autism Prediction System (RF-APS) for automatic ASD identification in this paper. These systems utilize characteristics extracted from the corpus callosum and intracranial brain volume, and are based on machine learning techniques. By prioritizing characteristics with the highest discriminatory power for ASD classification, our proposed approaches, DT-APS and RF-APS, have not only enhanced identification accuracy but also simplified the training of machine learning models. The initial step of this method involves dividing each MRI scan into distinct anatomical areas. These areas are adjacent slices in a single 2D image. Each 2D image is mapped to the curvelet space, and the set of GGD parameters characterizes each of the distinct curvelet sub-bands. The AQ-10 dataset was utilized to evaluate the proposed model. When tested on both types of datasets, the suggested prediction model demonstrated superior performance compared to alternative approaches in all relevant metrics, including accuracy, specificity, sensitivity, precision, and false positive rate (FPR).

Cite This Paper

Chitta Hrudaya Neeharika, Yeklur Mohammed Riyazuddin, "Unveiling Autism: Machine Learning-based Autism Spectrum Disorder Detection through MRI Analysis", International Journal of Information Technology and Computer Science(IJITCS), Vol.17, No.2, pp.10-23, 2025. DOI:10.5815/ijitcs.2025.02.02

Reference

[1]Al-Hiyali, Mohammed I., et al. "Autism spectrum disorder detection based on wavelet transform of BOLD fMRI signals using pre-trained convolution neural network." International Journal of Integrated Engineering 13.5 (2021): 49-56.
[2]Alvarez‐Jimenez, Charlems, et al. "Autism spectrum disorder characterization in children by capturing local‐regional brain changes in MRI." Medical physics 47.1 (2020): 119-131.
[3]Ashraf, Adnan, et al. "Analysis of Brain Imaging Data for the Detection of Early Age Autism Spectrum Disorder Using Transfer Learning Approaches for Internet of Things." IEEE Transactions on Consumer Electronics (2023).
[4]Craddock, R. Cameron, et al. "Disease state prediction from resting state functional connectivity." Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 62.6 (2009): 1619-1628.
[5]Erkan, Uğur, and Dang NH Thanh. "Autism spectrum disorder detection with machine learning methods." Current Psychiatry Research and Reviews Formerly: Current Psychiatry Reviews 15.4 (2019): 297-308.
[6]Heinsfeld, Anibal Sólon, et al. "Identification of autism spectrum disorder using deep learning and the ABIDE dataset." NeuroImage: Clinical 17 (2018): 16-23.
[7]Khodatars, M., et al. "Deep learning for neuroimaging-based diagnosis and rehabilitation of autism spectrum disorder: A review. arXiv 2020." arXiv preprint arXiv:2007.01285 (2007).
[8]Koyamada, Koji, et al. "Data-driven derivation of partial differential equations using neural network model." International Journal of Modeling, Simulation, and Scientific Computing 12.02 (2021): 2140001.
[9]Kucharsky Hiess, R., et al. "Corpus callosum area and brain volume in autism spectrum disorder: quantitative analysis of structural MRI from the ABIDE database." Journal of autism and developmental disorders 45 (2015): 3107-3114.
[10]Mazumdar, Arpita, et al. "Machine learning based autism screening tool—a modified approach." Multimedia Tools and Applications (2024): 1-18.
[11]Mishra, Mayank, and Umesh C. Pati. "A classification framework for Autism Spectrum Disorder detection using sMRI: Optimizer based ensemble of deep convolution neural network with on-the-fly data augmentation." Biomedical Signal Processing and Control 84 (2023): 104686.
[12]Nogay, Hidir Selcuk, and Hojjat Adeli. "Machine learning (ML) for the diagnosis of autism spectrum disorder (ASD) using brain imaging." Reviews in the Neurosciences 31.8 (2020): 825-841.
[13]Nogay, Hidir Selcuk, and Hojjat Adeli. "Multiple Classification of Brain MRI Autism Spectrum Disorder by Age and Gender Using Deep Learning." Journal of Medical Systems 48.1 (2024): 15.
[14]Omar, Kazi Shahrukh, et al. "A machine learning approach to predict autism spectrum disorder." 2019 International conference on electrical, computer and communication engineering (ECCE). IEEE, 2019.
[15]Parlett-Pelleriti, Chelsea M., et al. "Applications of unsupervised machine learning in autism spectrum disorder research: a review." Review Journal of Autism and Developmental Disorders 10.3 (2023): 406-421.
[16]Plis, Sergey M., et al. "Deep learning for neuroimaging: a validation study." Frontiers in neuroscience 8 (2014): 92071.
[17]Sabegh, Amin Majidzadeh, et al. "Automatic detection of autism spectrum disorder based on fMRI images using a novel convolutional neural network." Research on Biomedical Engineering 39.2 (2023): 407-413.
[18]Sabuncu, Mert R., and Koen Van Leemput. "The relevance voxel machine (RVoxM): a self-tuning Bayesian model for informative image-based prediction." IEEE transactions on medical imaging31.12 (2012): 2290-2306.
[19]Shen, Yanyong, et al. "Exploring White Matter Abnormalities in Young Children with Autism Spectrum Disorder: Integrating Multi-shell Diffusion Data and Machine Learning Analysis." Academic Radiology (2024).
[20]Uddin, Md Zasim, et al. "Deep learning with image-based autism spectrum disorder analysis: A systematic review." Engineering Applications of Artificial Intelligence 127 (2024): 107185.
[21]Wei, Qiuhong, et al. "Machine learning based on eye-tracking data to identify Autism Spectrum Disorder: A systematic review and meta-analysis." Journal of biomedical informatics 137 (2023): 104254.
[22]Xue, Feng, et al. "Real time biped walking gait pattern generator for a real robot." RoboCup 2011: Robot Soccer World Cup XV 15. Springer Berlin Heidelberg, 2012.
[23]Yakolli, Nivedan, et al. "Enhancing the diagnosis of autism spectrum disorder using phenotypic, structural, and functional MRI data." Innovations in Systems and Software Engineering (2023): 1-12.