ResNeXt convolutional forward harmonic network for autism spectrum disorder detection in infant

Abstract

The brain is an incredible example of biological engineering, managing human cognition, behavior, and perception. Autism Spectrum Disorder (ASD) emerges within this complex organ. It brings a variety of challenges and distinct traits that influence social interactions, communication, and sensory experiences. ASD is a developmental condition that appears in early childhood, impacting how individuals perceive and engage with their surroundings. Detecting ASD involves observing behavioral patterns, assessing communication skills, and recognizing specific behavioral traits. In this work, the ASD detection is done using the ResNeXt Convolutional Forward Harmonic Network (ResCFHN). At first, the input image is acquired from the specified dataset and is pre-processed using the Region of Interest (RoI) and Kuwahara filter techniques. Then, the extraction and identification of pivotal region is done using the Box Neighborhood Search Algorithm based on functional connectivity. Thereafter, the ResCFHN is used for performing ASD detection. The proposed ResCFHN is obtained by integrating ResNeXt with Convolutional Neural Network (CNN). The ResCFHN has achieved 91.094% of accuracy, with 90.774% of sensitivity and 92.879% of specificity.

Keywords

autism spectrum disorder (ASD) detection ResNeXt convolutional neural network (CNN)Kuwahara filter box neighborhood search algorithm

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References

Yang

Islam

Khaled

. Functional connectivity magnetic resonance imaging classification of autism spectrum disorder using the multisite ABIDE dataset. In: 2019 IEEE EMBS international conference on biomedical & health informatics (BHI), IEEE, May 2019, pp.1–4.

, et al. Autism spectrum disorder diagnosis with EEG signals using time series maps of brain functional connectivity and a combined CNN–LSTM model. Comput Methods Prog Biomed 2024; 250: 108196.

Choi

Kang

, et al. Exploring the structural and strategic bases of autism spectrum disorders with deep learning. IEEE Access 2020; 8: 153341–153352.

Zwaigenbaum

Bauman

Stone

, et al. Early identification of autism spectrum disorder: recommendations for practice and research. Pediatrics 2015; 136: S10–S40.

Almars

Badawy

Elhosseini

. ASD2-TL∗ GTO: Autism spectrum disorders detection via transfer learning with gorilla troops optimizer framework. Heliyon 2023; 9: e21530

Alkahtani

Aldhyani

Alzahrani

. Deep learning algorithms to identify autism spectrum disorder in children-based facial landmarks. Appl Sci 2023; 13: 4855.

Zaky

. Autism spectrum disorder (ASD): The past, the present, and the future. J Child Adolesc Behav 2017; 5: e116.

Koc

Kalkan

Bilgen

. Autism spectrum disorder detection by hybrid convolutional recurrent neural networks from structural and resting state functional MRI images. Autism Res Treat 2023; 2023: 4136087.

Maghdid

Asaad

Ghafoor

, et al. Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms. In: Multimodal image exploitation and learning 2021, SPIE, April 2021, vol. 11734, pp.99–110.

10.

Pathan

Siddalingaswamy

Kumar

, et al. Novel ensemble of optimized CNN and dynamic selection techniques for accurate Covid-19 screening using chest CT images. Comput Biol Med 2021; 137: 104835.

11.

LeCun

Bottou

Bengio

, et al. Gradient-based learning applied to document recognition. Proc IEEE 1998; 86: 2278–2324.

12.

Kuttala

Mahapatra

Subramanian

, et al. Dense attentive GAN-based one-class model for detection of autism and ADHD. J King Saud Univ-Comput Inf Sci 2022; 34: 10444–10458.

13.

Raja

Kannimuthu

. Conditional generative adversarial network approach for autism prediction. Comput Syst Sci Eng 2023; 44: 741–755.

14.

Sadiq

Al-Hiyali

Yahya

, et al. Non-oscillatory connectivity approach for classification of autism spectrum disorder subtypes using resting-state fMRI. IEEE Access 2022; 10: 14049–14061.

15.

Jain

Tripathy

Mallik

, et al. Autism detection of MRI brain images using hybrid deep CNN with DM-Resnet classifier. IEEE Access 2023; 11: 117741–117751.

16.

Sun

. Deep multi-instance conv-transformer frameworks for landmark-based brain MRI classification. Electronics 2024; 13: 980.

17.

Benabdallah

Drissi El Maliani

Lotfi

, et al. A convolutional neural network-based connectivity enhancement approach for autism spectrum disorder detection. J Imag 2023; 9: 110.

18.

Neeharika

Riyazuddin

. Developing an artificial intelligence based model for autism spectrum disorder detection in children. J Adv Res Appl Sci Eng Technol 2023; 32: 57–72.

19.

The ABIDE database taken from, https://github.com/lsa-pucrs/acerta-abide (accessed July 2024).

20.

Bartyzel

. Adaptive kuwahara filter. Signal Image Video Process 2016; 10: 663–670.

21.

Anitha

Rao

. Extraction of region of interest (ROI) for palm print and inner knuckle print. Int J Comput Appl 2015; 124.

22.

Heinsfeld

Franco

Craddock

, et al. Identification of autism spectrum disorder using deep learning and the ABIDE dataset. NeuroImage: Clin 2018; 17: 16–23.

23.

Kim

Lee

. Abnormality detection in chest X-ray via residual-saliency from normal generation. IEEE Access 2023; 11: 21799–21810.

24.

Hitawala

. Evaluating resnext model architecture for image classification, arXiv preprint arXiv: 1805.08700, 2018.

25.

Acharya

Hagiwara

, et al. A deep convolutional neural network model to classify heartbeats. Comput Biol Med 2017; 89: 389–396.

26.

Nguyen

Cheah

. A layer-wise theoretical framework for deep learning of convolutional neural networks. IEEE Access 2022; 10: 14270–14287.

27.

Xie

Girshick

Dollár

, et al. Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp.1492–1500.

28.

Damsleth

Spjøtvoll

. Estimation of trigonometric components in time series. J Am Stat Assoc 1982; 77: 381–387.

29.

Natarajan

Elakkiya

Bhuvaneswari

, et al. Creating alert messages based on wild animal activity detection using hybrid deep neural networks. IEEE Access 2023; 11: 67308–67321.