Advancements in automated diagnosis of autism spectrum disorder through deep learning and resting-state functional mri biomarkers: a systematic review

Aghdam MA, Sharifi A, Pedram MM (2019) Diagnosis of autism spectrum disorders in young children based on resting-state functional magnetic resonance imaging data using convolutional neural networks. J Digit Imaging 32:899–918

Article  PubMed  PubMed Central  Google Scholar 

Ahammed MS, Niu S, Ahmed MR et al (2021) Darkasdnet: classification of asd on functional mri using deep neural network. Front Neuroinform 15:635657

Article  PubMed  PubMed Central  Google Scholar 

Al-Hiyali MI, Yahya N, Faye I, et al (2021) Classification of bold fmri signals using wavelet transform and transfer learning for detection of autism spectrum disorder. In 2020 IEEE-EMBS conference on biomedical engineering and sciences (IECBES), IEEE, 94–98

Almuqhim F, Saeed F (2021) Asd-saenet: a sparse autoencoder, and deep-neural network model for detecting autism spectrum disorder (asd) using fmri data. Front Comput Neurosci 15:654315

Article  PubMed  PubMed Central  Google Scholar 

Alsaade FW, Alzahrani MS et al (2022) Classification and detection of autism spectrum disorder based on deep learning algorithms. Comput Intell Neurosci 2022(1):8709145

PubMed  PubMed Central  Google Scholar 

Aslam F, Khan Z, Tahir A, et al (2022) A survey of deep learning methods for fruit and vegetable detection and yield estimation. In Big data analytics and computational intelligence for cybersecurity. Springer, 299–323

Bayram MA, İlyas Ö, Temurtaş F (2021) Deep learning methods for autism spectrum disorder diagnosis based on fmri images. Sakarya Univ J Comput Inf Sci 4(1):142–155

Google Scholar 

Benabdallah FZ, Drissi El Maliani A, Lotfi D et al (2023) A convolutional neural network-based connectivity enhancement approach for autism spectrum disorder detection. J Imag 9(6):110

Article  Google Scholar 

El Gazzar A, Cerliani L, van Wingen G, et al (2019) Simple 1-d convolutional networks for resting-state fmri based classification in autism. In 2019 International joint conference on neural networks (IJCNN), IEEE, 1–6

Eslami T, Mirjalili V, Fong A et al (2019) Asd-diagnet: a hybrid learning approach for detection of autism spectrum disorder using fmri data. Front Neuroinform 13:70

Article  PubMed  PubMed Central  Google Scholar 

Feng M, Xu J (2023) Detection of asd children through deep-learning application of fmri. Children 10(10):1654

Article  PubMed  PubMed Central  Google Scholar 

GeeksforGeeks (2023) Introduction to ann | set 4 (network architectures). https://www.geeksforgeeks.org/introduction-to-ann-set-4-network-architectures/, accessed: 2024-07-18

Gill NS (2023) Artificial neural network applications. https://www.xenonstack.com/blog/artificial-neural-network-applications, accessed: 2024-07-18

Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182

Google Scholar 

Heinsfeld AS, Franco AR, Craddock RC et al (2018) Identification of autism spectrum disorder using deep learning and the abide dataset. NeuroImage Clinic 17:16–23

Article  Google Scholar 

Jain S, Tripathy HK, Mallik S et al (2023) Autism detection of mri brain images using hybrid deep cnn with dm-resnet classifier. IEEE Access 11:117741–117751

Article  Google Scholar 

Jain V, Sengar SS, Ronickom JFA (2023b) Age-specific diagnostic classification of asd using deep learning approaches. In Telehealth ecosystems in practice: proceedings of the EFMI special topic conference 2023, IOS Press, p 267

Karuppasamy SG, Muralitharan D, Gowr S, et al (2022) Prediction of autism spectrum disorder using convolution neural network. In 2022 6th international conference on trends in electronics and informatics (ICOEI), IEEE, 1096–1100

Khan DM, Yahya N, Kamel N et al (2021) Automated diagnosis of major depressive disorder using brain effective connectivity and 3d convolutional neural network. Ieee Access 9:8835–8846

Article  Google Scholar 

Khan DM, Yahya N, Kamel N et al (2021) Effective connectivity in default mode network for alcoholism diagnosis. IEEE Trans Neural Syst Rehabil Eng 29:796–808

Article  PubMed  Google Scholar 

Khan DM, Masroor K, Jailani MFM et al (2022) Development of wavelet coherence eeg as a biomarker for diagnosis of major depressive disorder. IEEE Sens J 22(5):4315–4325

Article  Google Scholar 

Khan DM, Yahya N, Kamel N et al (2023) A novel method for efficient estimation of brain effective connectivity in eeg. Comput Methods Programs Biomed 228:107242

Article  PubMed  Google Scholar 

Khodatars M, Shoeibi A, Sadeghi D et al (2021) Deep learning for neuroimaging-based diagnosis and rehabilitation of autism spectrum disorder: a review. Comput Biol Med 139:104949

Article  PubMed  Google Scholar 

Lamani MR, Benadit PJ, Vaithinathan K (2023) Autism spectrum disorder: automated detection based on rs-fmri images using cnn. In 2023 IEEE international conference on contemporary computing and communications (InC4), IEEE, 1–5

Liang Y, Liu B, Zhang H (2021) A convolutional neural network combined with prototype learning framework for brain functional network classification of autism spectrum disorder. IEEE Trans Neural Syst Rehabil Eng 29:2193–2202

Article  PubMed  Google Scholar 

Liu M, Li B, Hu D (2021) Autism spectrum disorder studies using fmri data and machine learning: a review. Front Neurosci 15:697870

Article  PubMed  PubMed Central  Google Scholar 

Memon M (2022) Neural networks: Cnn, ann, rnn. https://levity.ai/blog/neural-networks-cnn-ann-rnn, accessed: 2024-07-18

Moridian P, Ghassemi N, Jafari M et al (2022) Automatic autism spectrum disorder detection using artificial intelligence methods with mri neuroimaging: a review. Front Mol Neurosci 15:999605

Article  PubMed  PubMed Central  Google Scholar 

Nasser IM, Al-Shawwa M, Abu-Naser SS (2019) Artificial neural network for diagnose autism spectrum disorder. Int J Acad Inf Syst Res (IJAISR) 3(2):27–31

Google Scholar 

Sabegh AM, Samadzadehaghdam N, Seyedarabi H et al (2023) Automatic detection of autism spectrum disorder based on fmri images using a novel convolutional neural network. Res Biomed Eng 39:1–7

Article  Google Scholar 

Sabir MW, Khan Z, Saad NM et al (2022) Segmentation of liver tumor in ct scan using resu-net. Appl Sci 12(17):8650

Article  CAS  Google Scholar 

Sadiq A, Al-Hiyali MI, Yahya N et al (2022) Non-oscillatory connectivity approach for classification of autism spectrum disorder subtypes using resting-state fmri. IEEE Access 10:14049–14061

Article  Google Scholar 

Santana CP, de Carvalho EA, Rodrigues ID et al (2022) rs-fmri and machine learning for asd diagnosis: a systematic review and meta-analysis. Sci Rep 12(1):6030

Article  CAS  PubMed  PubMed Central  Google Scholar 

Sarkis-Onofre R, Catalá-López F, Aromataris E et al (2021) How to properly use the prisma statement. Syst Rev 10(1):1–3

Article  Google Scholar 

Shao L, Fu C, You Y et al (2021) Classification of asd based on fmri data with deep learning. Cogn Neurodyn 15(6):961–974

Article  PubMed  PubMed Central  Google Scholar 

Sherkatghanad Z, Akhondzadeh M, Salari S et al (2020) Automated detection of autism spectrum disorder using a convolutional neural network. Front Neurosci 13:1325

Article  PubMed  PubMed Central  Google Scholar 

Subah FZ, Deb K, Dhar PK et al (2021) A deep learning approach to predict autism spectrum disorder using multisite resting-state fmri. Appl Sci 11(8):3636

Article  CAS  Google Scholar 

Thomas RM, Gallo S, Cerliani L et al (2020) Classifying autism spectrum disorder using the temporal statistics of resting-state functional mri data with 3d convolutional neural networks. Front Psych 11:440

Article  Google Scholar 

Wang C (2021) Indentification of autism spectrum disorder based on an improved convolutional neural networks. In 2021 3rd International conference on machine learning. Big data and business intelligence (MLBDBI), IEEE, pp 235–239

Xu M, Calhoun V, Jiang R et al (2021) Brain imaging-based machine learning in autism spectrum disorder: methods and applications. J Neurosci Methods 361:109271

Article  PubMed  PubMed Central  Google Scholar 

Yang X, Islam MS, Khaled AA (2019) 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, 1–4

Yang X, Zhang N, Schrader P (2022) A study of brain networks for autism spectrum disorder classification using resting-state functional connectivity. Mach Learn Appl 8:100290

Google Scholar 

Yin W, Mostafa S, Wu FX (2021) Diagnosis of autism spectrum disorder based on functional brain networks with deep learning. J Comput Biol 28(2):146–165

Article  CAS  PubMed  Google Scholar 

Comments (0)

No login
gif