{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T20:31:18Z","timestamp":1773693078015,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,8,29]],"date-time":"2021-08-29T00:00:00Z","timestamp":1630195200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Deanship of Scientific Research -  the Fast-track Research Funding Program.","award":["N\/A"],"award-info":[{"award-number":["N\/A"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Autism spectrum disorder (ASD) is a neurodegenerative disorder characterized by lingual and social disabilities. The autism diagnostic observation schedule is the current gold standard for ASD diagnosis. Developing objective computer aided technologies for ASD diagnosis with the utilization of brain imaging modalities and machine learning is one of main tracks in current studies to understand autism. Task-based fMRI demonstrates the functional activation in the brain by measuring blood oxygen level-dependent (BOLD) variations in response to certain tasks. It is believed to hold discriminant features for autism. A novel computer aided diagnosis (CAD) framework is proposed to classify 50 ASD and 50 typically developed toddlers with the adoption of CNN deep networks. The CAD system includes both local and global diagnosis in a response to speech task. Spatial dimensionality reduction with region of interest selection and clustering has been utilized. In addition, the proposed framework performs discriminant feature extraction with continuous wavelet transform. Local diagnosis on cingulate gyri, superior temporal gyrus, primary auditory cortex and angular gyrus achieves accuracies ranging between 71% and 80% with a four-fold cross validation technique. The fused global diagnosis achieves an accuracy of 86% with 82% sensitivity, 92% specificity. A brain map indicating ASD severity level for each brain area is created, which contributes to personalized diagnosis and treatment plans.<\/jats:p>","DOI":"10.3390\/s21175822","type":"journal-article","created":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T22:58:15Z","timestamp":1630450695000},"page":"5822","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A CNN Deep Local and Global ASD Classification Approach with Continuous Wavelet Transform Using Task-Based FMRI"],"prefix":"10.3390","volume":"21","author":[{"given":"Reem","family":"Haweel","sequence":"first","affiliation":[{"name":"Faculty of Computer and Information Sciences, University of Ain Shams, Cairo 11566, Egypt"},{"name":"Bioengineering Department, University of Louisville, Louisville, KY 40208, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Noha","family":"Seada","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Information Sciences, University of Ain Shams, Cairo 11566, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Said","family":"Ghoniemy","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Information Sciences, University of Ain Shams, Cairo 11566, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6421-6001","authenticated-orcid":false,"given":"Norah Saleh","family":"Alghamdi","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ayman","family":"El-Baz","sequence":"additional","affiliation":[{"name":"Bioengineering Department, University of Louisville, Louisville, KY 40208, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.tins.2007.12.005","article-title":"Neuroanatomy of autism","volume":"31","author":"Amaral","year":"2008","journal-title":"Trends Neurosci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e1278","DOI":"10.1542\/peds.2011-3668","article-title":"Trajectories of autism severity in children using standardized ADOS scores","volume":"130","author":"Gotham","year":"2012","journal-title":"Pediatrics"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s10803-008-0674-3","article-title":"Standardizing ADOS scores for a measure of severity in autism spectrum disorders","volume":"39","author":"Gotham","year":"2009","journal-title":"J. Autism Dev. Disord."},{"key":"ref_4","first-page":"2","article-title":"Autism spectrum disorders","volume":"43","author":"Murray","year":"2013","journal-title":"Curr. Probl. Pediatr. Adolesc. Health Care"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.ijdevneu.2004.05.001","article-title":"Behavioral manifestations of autism in the first year of life","volume":"23","author":"Zwaigenbaum","year":"2005","journal-title":"Int. J. Dev. Neurosci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Casanova, M.F., El-Baz, A., and Suri, J.S. (2017). Autism Imaging and Devices, CRC Press.","DOI":"10.1201\/9781315371375"},{"key":"ref_7","unstructured":"Ismail, M.M.T. (2016). A CAD System for Early Diagnosis of Autism Using Different Imaging Modalities. [Ph.D. Thesis, University of Louisville]."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1098\/rstb.2001.0916","article-title":"The Functional Magnetic Resonance Imaging Data Center (fMRIDC): The challenges and rewards of large\u2013scale databasing of neuroimaging studies","volume":"356","author":"Grethe","year":"2001","journal-title":"Philos. Trans. R. Soc. Lond. B Biol. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Casanova, M.F., El-Baz, A.S., and Suri, J.S. (2013). Imaging the Brain in Autism, Springer.","DOI":"10.1007\/978-1-4614-6843-1"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1016\/j.neubiorev.2011.10.008","article-title":"A systematic review and meta-analysis of the fMRI investigation of autism spectrum disorders","volume":"36","author":"Philip","year":"2012","journal-title":"Neurosci. Biobehav. Rev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1093\/brain\/awr364","article-title":"A failure of left temporal cortex to specialize for language is an early emerging and fundamental property of autism","volume":"135","author":"Eyler","year":"2012","journal-title":"Brain"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.1038\/s41593-018-0281-3","article-title":"Large-scale associations between the leukocyte transcriptome and BOLD responses to speech differ in autism early language outcome subtypes","volume":"21","author":"Lombardo","year":"2018","journal-title":"Nat. Neurosci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1016\/j.neuron.2015.03.023","article-title":"Different functional neural substrates for good and poor language outcome in autism","volume":"86","author":"Lombardo","year":"2015","journal-title":"Neuron"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1007\/s11065-013-9234-5","article-title":"Atypicalities in cortical structure, handedness, and functional lateralization for language in autism spectrum disorders","volume":"23","author":"Lindell","year":"2013","journal-title":"Neuropsychol. Rev."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.dcn.2012.11.007","article-title":"Atypical lateralization of ERP response to native and non-native speech in infants at risk for autism spectrum disorder","volume":"5","author":"Seery","year":"2013","journal-title":"Dev. Cogn. Neurosci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"223","DOI":"10.2217\/npy.13.19","article-title":"Speech and language in autism spectrum disorder: A view through the lens of behavior and brain imaging","volume":"3","author":"Mody","year":"2013","journal-title":"Neuropsychiatry"},{"key":"ref_17","first-page":"23","article-title":"A review on autism spectrum disorder diagnosis using task-based functional mri","volume":"21","author":"Haweel","year":"2021","journal-title":"Int. J. Intell. Comput. Inf. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhuang, J., Dvornek, N.C., Li, X., Yang, D., Ventola, P., and Duncan, J.S. (2018, January 4\u20137). Prediction of pivotal response treatment outcome with task fMRI using random forest and variable selection. Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA.","DOI":"10.1109\/ISBI.2018.8363531"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Haweel, R., Dekhil, O., Shalaby, A., Mahmoud, A., Ghazal, M., Khalil, A., Ghoniemy, S., Keynton, R., Elmaghraby, A., and Barnes, G. (2019, January 17\u201319). Functional magnetic resonance imaging based framework for autism diagnosis. Proceedings of the 2019 Fifth International Conference on Advances in Biomedical Engineering (ICABME), Tripoli, Lebanon.","DOI":"10.1109\/ICABME47164.2019.8940348"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Haweel, R., Dekhil, O., Shalaby, A., Mahmoud, A., Ghazal, M., Khalil, A., Keynton, R., Barnes, G., and El-Baz, A. (2020, January 3\u20137). A Novel Framework for Grading Autism Severity Using Task-Based FMRI. Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, USA.","DOI":"10.1109\/ISBI45749.2020.9098430"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Haweel, R., Dekhil, O., Shalaby, A., Mahmoud, A., Ghazal, M., Keynton, R., Barnes, G., and El-Baz, A. (2019, January 9\u201310). A Machine Learning Approach for Grading Autism Severity Levels Using Task-based Functional MRI. Proceedings of the International Conference on Imaging Systems and Techniques (IST\u201919), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/IST48021.2019.9010335"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"100570","DOI":"10.1109\/ACCESS.2021.3097606","article-title":"A Novel Grading System for Autism Severity Level Using Task-based Functional MRI: A Response to Speech Study","volume":"9","author":"Haweel","year":"2021","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, R., Xu, P., Guo, L., Zhang, Y., Li, P., and Yao, D. (2013). Z-score linear discriminant analysis for EEG based brain-computer interfaces. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0074433"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Bai, J., Ding, B., Xiao, Z., Jiao, L., Chen, H., and Regan, A.C. (2021). Hyperspectral Image Classification Based on Deep Attention Graph Convolutional Network. IEEE Trans. Geosci. Remote Sens., 1\u201316.","DOI":"10.1109\/TGRS.2021.3066485"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.cmpb.2004.10.009","article-title":"Classification of EEG signals using neural network and logistic regression","volume":"78","author":"Subasi","year":"2005","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"23","DOI":"10.3389\/fninf.2018.00023","article-title":"Deep learning methods to process fMRI data and their application in the diagnosis of cognitive impairment: A brief overview and our opinion","volume":"12","author":"Wen","year":"2018","journal-title":"Front. Neuroinform."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5065214","DOI":"10.1155\/2019\/5065214","article-title":"A Multichannel 2D Convolutional Neural Network Model for Task-Evoked fMRI Data Classification","volume":"2019","author":"Hu","year":"2019","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, X., Dvornek, N.C., Papademetris, X., Zhuang, J., Staib, L.H., Ventola, P., and Duncan, J.S. (2018, January 4\u20137). 2-channel convolutional 3D deep neural network (2CC3D) for fMRI analysis: ASD classification and feature learning. Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA.","DOI":"10.1109\/ISBI.2018.8363798"},{"key":"ref_29","first-page":"63","article-title":"Automatic machine fault diagnosis based on wavelet transform and probabilistic neural networks","volume":"14","author":"Amin","year":"2014","journal-title":"Int. J. Intell. Comput. Inf. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.jneumeth.2010.09.005","article-title":"Wavelet correlation between subjects: A time-scale data driven analysis for brain mapping using fMRI","volume":"194","author":"Lessa","year":"2011","journal-title":"J. Neurosci. Methods"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"030003","DOI":"10.1063\/1.4954096","article-title":"Diagnosis of ADHD children by wavelet analysis","volume":"1747","author":"Barbosa","year":"2016","journal-title":"AIP Conf. Proc."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.nicl.2015.11.010","article-title":"Classification of autistic individuals and controls using cross-task characterization of fMRI activity","volume":"10","author":"Chanel","year":"2016","journal-title":"NeuroImage Clin."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Dvornek, N.C., Yang, D., Ventola, P., and Duncan, J.S. (2018, January 16\u201320). Learning Generalizable Recurrent Neural Networks from Small Task-fMRI Datasets. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Granada, Spain.","DOI":"10.1007\/978-3-030-00931-1_38"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1001\/jama.297.4.403","article-title":"Practice-based research\u2014\u201cBlue Highways\u201d on the NIH roadmap","volume":"297","author":"Westfall","year":"2007","journal-title":"JAMA"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s12021-012-9151-4","article-title":"Sharing heterogeneous data: The national database for autism research","volume":"10","author":"Hall","year":"2012","journal-title":"Neuroinformatics"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1370","DOI":"10.1006\/nimg.2001.0931","article-title":"Temporal autocorrelation in univariate linear modeling of FMRI data","volume":"14","author":"Woolrich","year":"2001","journal-title":"Neuroimage"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1016\/j.neuroimage.2011.09.015","article-title":"Fsl","volume":"62","author":"Jenkinson","year":"2012","journal-title":"Neuroimage"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2315","DOI":"10.1002\/mp.14692","article-title":"A robust DWT\u2013CNN-based CAD system for early diagnosis of autism using task-based fMRI","volume":"48","author":"Haweel","year":"2020","journal-title":"Med. Phys."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/17\/5822\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:55:09Z","timestamp":1760165709000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/17\/5822"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,29]]},"references-count":39,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["s21175822"],"URL":"https:\/\/doi.org\/10.3390\/s21175822","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,29]]}}}