{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T23:52:53Z","timestamp":1793404373963,"version":"4.5.0"},"reference-count":75,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T00:00:00Z","timestamp":1705881600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T00:00:00Z","timestamp":1705881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Med Syst"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The fact that the rapid and definitive diagnosis of autism cannot be made today and that autism cannot be treated provides an impetus to look into novel technological solutions. To contribute to the resolution of this problem through multiple classifications by considering age and gender factors, in this study, two quadruple and one octal classifications were performed using a deep learning (DL) approach. Gender in one of the four classifications and age groups in the other were considered. In the octal classification, classes were created considering gender and age groups. In addition to the diagnosis of ASD (Autism Spectrum Disorders), another goal of this study is to find out the contribution of gender and age factors to the diagnosis of ASD by making multiple classifications based on age and gender for the first time. Brain structural MRI (sMRI) scans of participators with ASD and TD (Typical Development) were pre-processed in the system originally designed for this purpose. Using the Canny Edge Detection (CED) algorithm, the sMRI image data was cropped in the data pre-processing stage, and the data set was enlarged five times with the data augmentation (DA) techniques. The most optimal convolutional neural network (CNN) models were developed using the grid search optimization (GSO) algorism. The proposed DL prediction system was tested with the five-fold cross-validation technique. Three CNN models were designed to be used in the system. The first of these models is the quadruple classification model created by taking gender into account (model 1), the second is the quadruple classification model created by taking into account age (model 2), and the third is the eightfold classification model created by taking into account both gender and age (model 3). ). The accuracy rates obtained for all three designed models are 80.94, 85.42 and 67.94, respectively. These obtained accuracy rates were compared with pre-trained models by using the transfer learning approach. As a result, it was revealed that age and gender factors were effective in the diagnosis of ASD with the system developed for ASD multiple classifications, and higher accuracy rates were achieved compared to pre-trained models.<\/jats:p>","DOI":"10.1007\/s10916-023-02032-0","type":"journal-article","created":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T02:03:31Z","timestamp":1705889011000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":120,"title":["Multiple Classification of Brain MRI Autism Spectrum Disorder by Age and Gender Using Deep Learning"],"prefix":"10.1007","volume":"48","author":[{"given":"Hidir Selcuk","family":"Nogay","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hojjat","family":"Adeli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,22]]},"reference":[{"key":"2032_CR1","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.ajp.2016.07.021","volume":"25","author":"K Preet","year":"2017","unstructured":"Preet K, Shoba S, Shekhar PS, Satish CG, and John VSK (2017) Lost time: Need for more awareness in early intervention of autism spectrum disorder. Asian Journal of Psychiatry 25:13\u201315","journal-title":"Asian Journal of Psychiatry"},{"key":"2032_CR2","doi-asserted-by":"crossref","unstructured":"Constantino JN, Charman T (2016) Diagnosis of autism spectrum disorder: reconciling the syndrome, its diverse origins, and variation in expression www.thelancet.com\/neurology 15","DOI":"10.1016\/S1474-4422(15)00151-9"},{"key":"2032_CR3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.15585\/mmwr.ss7011a1","volume":"70","author":"MJ Maenner","year":"2021","unstructured":"Maenner, M.J., Shaw, K.A., Bakian, A.V., Bilder, D.A., Durkin, M.S., Esler, A.N., Furnier, S.M., Hallas, L., Hall-Lande, J., Hudson, A., Hughes, M.M., Patrick, M.E., Pierce, K.J., Poynter, J.N., Salinas, A., Shenouda, J., Vehorn, A.C., Warren, Z., Constantino, J.N., Dirienzo, M., Fitzgerald, R.T., Grzybowski, A., Spivey, M.H., Pettygrove, S., Zahorodny, W., Ali, A.R., Andrews, J.G., Baroud, T., Gutierrez, J., Hewitt, A.S., Lee, L.C., Lopez, M.L., Mancilla, K.C., McArthur, D., Schwenk, Y.D., Washington, A., Williams, S., & Cogswell, M.E. (2021). Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years \u2014 Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2018. MMWR Surveillance Summaries 70: 1\u201316.","journal-title":"MMWR Surveillance Summaries"},{"key":"2032_CR4","unstructured":"Senn, M. CDC Estimate on Autism Prevalence Increases by Nearly 10%, to 1 in 54 Children in the U.S. (2020) Available online at: https:\/\/www.autismspeaks.org\/press-release\/cdc-estimate-autism-prevalence-increasesnearly-10-percent-1-54-children-us. Accessed April 26, 2020."},{"key":"2032_CR5","doi-asserted-by":"crossref","unstructured":"Jacob, S., Wolff, J.J., Steinbach, M.S., Doyle, C.B., Kumar, V., and Elison, J.T. (2019). Neurodevelopmental heterogeneity and computational approaches for understanding autism. Translational Psychiatry 9.","DOI":"10.1038\/s41398-019-0390-0"},{"key":"2032_CR6","doi-asserted-by":"crossref","first-page":"2475","DOI":"10.1016\/j.ridd.2013.05.021","volume":"34","author":"JL Matson","year":"2013","unstructured":"Matson, J. L., Rieske R. D., Williams, L.W (2013) The relationship between autism spectrum disorders and attention-deficit\/hyperactivity disorder: an overview. Research in Developmental Disabilities 34: 2475\u20132484.","journal-title":"Research in Developmental Disabilities"},{"key":"2032_CR7","doi-asserted-by":"crossref","first-page":"2020","DOI":"10.1186\/s13229-020-00342-5","volume":"11","author":"M Saito","year":"2020","unstructured":"Saito, M., Hirota, T., Sakamoto, Y., Adachi, M., Takahashi, M., Osato-Kaneda, A., Kim, Y.S., Leventhal, B.L., Shui, A.M., Kato, S., and Nakamura, K. (2020) Prevalence and cumulative incidence of autism spectrum disorders and the patterns of co-occurring neurodevelopmental disorders in a total population sample of 5-year-old children. Molecular Autism 11, 2020.","journal-title":"Molecular Autism"},{"key":"2032_CR8","unstructured":"Autism and Developmental Disabilities Monitoring Network Surveillance Year 2002 Principal Investigators (2007) Prevalence of autism spectrum disorders\u2014Autism and Developmental Disabilities Monitoring Network, 14 sites, United States, 2002. MMWR Surveill Summ 56: No. SS\u20131"},{"key":"2032_CR9","doi-asserted-by":"publisher","first-page":"889636","DOI":"10.3389\/fpsyt.2022.889636","volume":"13","author":"A Napolitano","year":"2022","unstructured":"Napolitano A, Schiavi S, La Rosa P, Rossi-Espagnet MC, Petrillo S, Bottino F, Tagliente E, Longo D, Lupi E, Casula L, Valeri G, Piemonte F, Trezza V and Vicari S. Sex (2022) Differences in Autism Spectrum Disorder: Diagnostic, Neurobiological, and Behavioral Features. Front. Psychiatry 13: 889636. doi: https:\/\/doi.org\/10.3389\/fpsyt.2022.889636","journal-title":"Front. Psychiatry"},{"key":"2032_CR10","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0106552","volume":"9","author":"C Horlin","year":"2014","unstructured":"Horlin C., Falkmer M., Parsons R., Albrecht MA, Falkmer T (2014) The cost of autism spectrum disorders. PloS One 9 e106552","journal-title":"PloS One"},{"issue":"Suppl 1","key":"2032_CR11","doi-asserted-by":"crossref","first-page":"3","DOI":"10.33588\/rn.60S01.2015019","volume":"60","author":"A Klin","year":"2015","unstructured":"Klin A., Klaiman, C., Jones, W (2015) Reducing age of autism diagnosis: developmental social neuroscience meets public health challenge. Revista de Neurologia 60(Suppl 1): 3\u201311","journal-title":"Revista de Neurologia"},{"key":"2032_CR12","doi-asserted-by":"crossref","first-page":"8775","DOI":"10.1016\/j.eswa.2015.07.031","volume":"42","author":"V Subbaraju","year":"2015","unstructured":"Subbaraju V, Sundaram S, Narasimham S, Suresh, MB (2015) Accurate detection of autism spectrum disorder from structural MRI using extended metacognitive radial basis function network. Expert Systems with Applications 42: 8775\u20138790, 2015.","journal-title":"Expert Systems with Applications"},{"key":"2032_CR13","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/j.neuroimage.2010.11.004","volume":"56","author":"S Lemm","year":"2011","unstructured":"Lemm S, Blankertz B, Dickhaus T, M\u00fcller KR (2011) Introduction to machine learning for brain imaging. NeuroImage 56: 387\u2013399","journal-title":"NeuroImage"},{"key":"2032_CR14","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The Elements of Statistical Learning","author":"T Hastie","year":"2009","unstructured":"Hastie T, Tibshirani R, Friedman J (2009) The Elements of Statistical Learning. 2nd edition, Springer, New York.","edition":"2"},{"key":"2032_CR15","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1023\/A:1005592401947","volume":"30","author":"C Lord","year":"2000","unstructured":"Lord C, Risi S, Lambrecht L, Cook EH, Leventhal BL, DiLavore PC Pickles A., Rutter M (2000) The autism diagnostic observation schedule-generic a standard measure of social and communication deficits associated with the spectrum of autism. J Autism Dev Disord 30: 205\u2013223","journal-title":"J Autism Dev Disord"},{"key":"2032_CR16","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1007\/BF02172145","volume":"24","author":"C Lord","year":"1994","unstructured":"Lord C, Rutter M, Le Couteur A (1994) Autism diagnostic interview-revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J Autism Dev Disord 24: 659\u2013685","journal-title":"J Autism Dev Disord"},{"key":"2032_CR17","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1001\/archgenpsychiatry.2011.148","volume":"69","author":"C Lord","year":"2012","unstructured":"Lord C, Petkova E, Hus V, Gan W, Lu F, Martin DM Ousley O, Guy L, Bernier R, Gerdts J. et al (2012) A multisite study of the clinical diagnosis of different autism spectrum disorders. Archives of General Psychiatry 69: 306\u2013313","journal-title":"Archives of General Psychiatry"},{"key":"2032_CR18","doi-asserted-by":"crossref","unstructured":"O\u2019Shea A, Ahmed R, Lightbody G, Mathieson SR, Pavlidis E, Lloyd RO Pisani F, Marnane WP, Boylan GB, & Temko A (2020) Deep Learning for EEG Seizure Detection in Preterm Infants. International journal of neural systems 2150008","DOI":"10.1142\/S0129065721500088"},{"key":"2032_CR19","doi-asserted-by":"crossref","unstructured":"Peng P, Xie L, & Wei H (2021) A Deep Fourier Neural Network for Seizure Prediction Using Convolutional Neural Network and Ratios of Spectral Power. International journal of neural systems 2150022","DOI":"10.1142\/S0129065721500222"},{"key":"2032_CR20","doi-asserted-by":"crossref","unstructured":"Yu Z, Albera L, Bouquin-Jeann\u00e8s RL, Kachenoura A, Karfoul A, Yang C, & Shu H (2022) Epileptic Seizure Prediction Using Deep Neural Networks Via Transfer Learning and Multi-Feature Fusion. International journal of neural systems 2250032","DOI":"10.1142\/S0129065722500320"},{"key":"2032_CR21","doi-asserted-by":"crossref","unstructured":"Bhattacharya A, Baweja T, & Karri SP (2021) Epileptic Seizure Prediction Using Deep Transformer Model. International journal of neural systems 2150058","DOI":"10.1142\/S0129065721500581"},{"key":"2032_CR22","doi-asserted-by":"crossref","unstructured":"Thangavel P, Thomas J, Peh WY, Jing J, Yuvaraj R, Cash SS, Chaudhari R, Karia S, Rathakrishnan R, Saini V, Shah N, Srivastava R, Tan Y, Westover B, & Dauwels J (2021) Time-Frequency Decomposition of Scalp Electroencephalograms Improves Deep Learning-Based Epilepsy Diagnosis. International journal of neural systems 2150032","DOI":"10.1142\/S0129065721500325"},{"key":"2032_CR23","doi-asserted-by":"crossref","unstructured":"Ozdemir M, Cura OK, and Akan A (2021) Epileptic EEG Classification by Using Time-Frequency Images for Deep Learning. International journal of neural systems 2150026.","DOI":"10.1142\/S012906572150026X"},{"key":"2032_CR24","doi-asserted-by":"publisher","unstructured":"Bone D, Goodwin MS, Black MP, Lee C, Audhkhasi K, and Narayanan S (2015) Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and promises. Journal of Autism Developmental Disorders 45(5):1121\u20131136. https:\/\/doi.org\/10.1007\/s10803-014-2268-6","DOI":"10.1007\/s10803-014-2268-6"},{"key":"2032_CR25","doi-asserted-by":"crossref","unstructured":"Ardakani HA, Taghizadeh M, and Shayegh F (2022) Diagnosis of Autism Disorder Based on Deep Network Trained by Augmented EEG Signals. International journal of neural systems 2250046.","DOI":"10.1142\/S0129065722500460"},{"key":"2032_CR26","doi-asserted-by":"crossref","unstructured":"Li S, Tang Z, Jin N, Yang Q, Liu G, Liu T, Hu J, Liu S, Wang P, Hao J, Zhang Z, Zhang X, Li J, Wang X, Li Z, Wang Y, Yang B, and Ma L (2022) Uncovering Brain Differences in Preschoolers and Young Adolescents with Autism Spectrum Disorder Using Deep Learning International journal of neural systems 2250044.","DOI":"10.1142\/S0129065722500447"},{"key":"2032_CR27","doi-asserted-by":"crossref","unstructured":"Rahman MM, Usman OL, Muniyandi RC, Sahran S, Mohamed S, & Razak RA (2020) A Review of Machine Learning Methods of Feature Selection and Classification for Autism Spectrum Disorder. Brain Sciences 10","DOI":"10.3390\/brainsci10120949"},{"issue":"11","key":"2032_CR28","first-page":"2250052","volume":"32","author":"B K\u00fc\u00e7\u00fckoglu","year":"2022","unstructured":"K\u00fc\u00e7\u00fckoglu B, Rueckauer B, Ahmad N, de Ruyter van Steveninck J, G\u00fc\u00e7l\u00fc U, & van Gerven MA (2022) Optimization of Neuroprosthetic Vision via End-to-end Deep Reinforcement Learning. bioRxiv 32(11): 2250052 (16 pages)","journal-title":"bioRxiv"},{"key":"2032_CR29","doi-asserted-by":"crossref","unstructured":"Xu F, Dong G, Li J, Yang Q, Wang L, Zhao Y, Yan Y, Zhao J, Pang S, Guo D, Zhang Y, & Leng J (2022) Deep Convolution Generative Adversarial Network-Based Electroencephalogram Data Augmentation for Post-Stroke Rehabilitation with Motor Imagery. International journal of neural systems 32(9): 2250039, (15 pages)","DOI":"10.1142\/S0129065722500393"},{"issue":"10","key":"2032_CR30","doi-asserted-by":"crossref","first-page":"2150046","DOI":"10.1142\/S0129065721500465","volume":"31","author":"G Alexandridis","year":"2021","unstructured":"Alexandridis G, Aliprantis J, Michalakis K, Korovesis K, Tsantilas P, & Caridakis G (2021) A Knowledge-Based Deep Learning Architecture for Aspect-Based Sentiment Analysis. International journal of neural systems 31(10): 2150046 (18 pages)","journal-title":"International journal of neural systems"},{"issue":"5","key":"2032_CR31","doi-asserted-by":"crossref","first-page":"2250021","DOI":"10.1142\/S0129065722500216","volume":"32","author":"A Olamat","year":"2022","unstructured":"Olamat A, Ozel P, and Atasever S (2022) Deep Learning Methods for Multi-Channel EEG-based Emotion Recognition. International Journal of Neural Systems 32(5): 2250021 (17 pages)","journal-title":"International Journal of Neural Systems"},{"issue":"6","key":"2032_CR32","doi-asserted-by":"crossref","first-page":"2250024","DOI":"10.1142\/S0129065722500241","volume":"32","author":"JD Lope","year":"2022","unstructured":"Lope JD, & Gra\u00f1a M, (2022) A Hybrid Time-Distributed Deep Neural Architecture for Speech Emotion Recognition. International journal of neural systems 32(6): 2250024 (14 pages)","journal-title":"International journal of neural systems"},{"issue":"1","key":"2032_CR33","doi-asserted-by":"crossref","first-page":"2150055","DOI":"10.1142\/S0129065721500556","volume":"32","author":"T Hu","year":"2021","unstructured":"Hu T, Xie L, Zhang L, Li G, & Yi Z (2021) Deep Multimodal Neural Network Based on Data-Feature Fusion for Patient-Specific Quality Assurance. International journal of neural systems 32 (1): 2150055","journal-title":"International journal of neural systems"},{"issue":"11","key":"2032_CR34","first-page":"2150056","volume":"31","author":"H Hashimoto","year":"2020","unstructured":"Hashimoto H, Kameda S, Maezawa H, Oshino S, Tani N, Khoo HM, Yanagisawa T, Yoshimine T, Kishima H, & Hirata M (2020) A Swallowing Decoder Based on Deep Transfer Learning: AlexNet Classification of the Intracranial Electrocorticogram. International journal of neural systems 31(11): 2150056 (16 pages)","journal-title":"International journal of neural systems"},{"issue":"9","key":"2032_CR35","doi-asserted-by":"crossref","first-page":"2150038","DOI":"10.1142\/S0129065721500386","volume":"31","author":"C Ieracitano","year":"2021","unstructured":"Ieracitano C, Morabito FC, Hussain A, & Mammone N (2021) A Hybrid-Domain Deep Learning-Based BCI For Discriminating Hand Motion Planning From EEG Sources. International journal of neural systems 31(9): 2150038 (20 pages)","journal-title":"International journal of neural systems"},{"key":"2032_CR36","doi-asserted-by":"crossref","first-page":"36879","DOI":"10.1109\/ACCESS.2021.3062709","volume":"9","author":"OL Usman","year":"2021","unstructured":"Usman OL, Muniyandi RC, Omar KB, & Mohamad M (2021) Advance Machine Learning Methods for Dyslexia Biomarker Detection: A Review of Implementation Details and Challenges. IEEE Access 9 36879\u201336897","journal-title":"IEEE Access"},{"key":"2032_CR37","volume":"12","author":"OL Usman","year":"2020","unstructured":"Usman OL, & Muniyandi RC (2020) CryptoDL: Predicting Dyslexia Biomarkers from Encrypted Neuroimaging Dataset Using Energy-Efficient Residue Number System and Deep Convolutional Neural Network. Symmetry 12, 836","journal-title":"Symmetry"},{"key":"2032_CR38","doi-asserted-by":"crossref","unstructured":"Usman OL, Muniyandi RC, Omar KB, and Mohamad M (2021) Gaussian smoothing and modified histogram normalization methods to improve neural-biomarker interpretations for dyslexia classification mechanism. PLoS ONE 16.","DOI":"10.1371\/journal.pone.0245579"},{"issue":"2","key":"2032_CR39","doi-asserted-by":"crossref","first-page":"191","DOI":"10.3233\/ICA-200640","volume":"28","author":"E Macias-Garcia","year":"2021","unstructured":"Macias-Garcia E, Galeana-Perez D, Medrano-Hermosillo J, and Bayro-Corrochano E (2021) Multi-stage Deep Learning Perception System for Mobile Robots. Integrated Computer-Aided Engineering 28(2): 191\u2013205","journal-title":"Integrated Computer-Aided Engineering"},{"issue":"3","key":"2032_CR40","doi-asserted-by":"crossref","first-page":"221","DOI":"10.3233\/ICA-210649","volume":"28","author":"J Gasienica-J\u00f3zkowy","year":"2021","unstructured":"Gasienica-J\u00f3zkowy J, Knapik M, and Cyganek B (2021) An ensemble deep learning method with optimized weights for drone-based water rescue and surveillance. Integrated Computer-Aided Engineering 28(3): 221\u2013235","journal-title":"Integrated Computer-Aided Engineering"},{"issue":"9","key":"2032_CR41","doi-asserted-by":"crossref","first-page":"1160","DOI":"10.1111\/mice.12850","volume":"37","author":"K Jiang","year":"2022","unstructured":"Jiang K, Han Q, and Du X (2022) Lost Data Neural Semantic Recovery Framework for Structural Health Monitoring Based on Deep Learning. Computer-Aided Civil and Infrastructure Engineering 37(9): 1160\u20131187","journal-title":"Computer-Aided Civil and Infrastructure Engineering"},{"issue":"10","key":"2032_CR42","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1111\/mice.12797","volume":"37","author":"X Pan","year":"2022","unstructured":"Pan X, and Yang TT (2022) Image-based monitoring of bolt loosening through deep-learning-based integrated detection and tracking. Computer-Aided Civil and Infrastructure Engineering 37(10): 1207\u20131222","journal-title":"Computer-Aided Civil and Infrastructure Engineering"},{"issue":"11","key":"2032_CR43","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.1111\/mice.12793","volume":"37","author":"PJ Chun","year":"2022","unstructured":"Chun PJ, Yamane T, and Maemura Y (2022) A deep learning based image captioning method to automatically generate comprehensive explanations of bridge damage. Computer-Aided Civil and Infrastructure Engineering 37(11): 1387\u20131401","journal-title":"Computer-Aided Civil and Infrastructure Engineering"},{"key":"2032_CR44","doi-asserted-by":"publisher","unstructured":"Rad NM, and Furlanello C (2016) Applying Deep Learning to Stereotypical Motor Movement Detection in Autism Spectrum Disorders. 2016 IEEE 16th International Conference on Data Mining Workshops 16: 2375\u20139259, DOI https:\/\/doi.org\/10.1109\/ICDMW.2016.184","DOI":"10.1109\/ICDMW.2016.184"},{"key":"2032_CR45","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.sigpro.2017.10.011","volume":"144","author":"NM Rad","year":"2018","unstructured":"Rad NM, Kia MS, Zarbo C, Laarhoven TV, Jurman G, Venuti P, Marchiori E, Furlanello C (2018) Deep Learning for Automatic Stereotypical Motor Movement Detection using Wearable Sensors in Autism Spectrum Disorder. Signal Processing 144: 180\u2013191","journal-title":"Signal Processing"},{"issue":"3","key":"2032_CR46","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1016\/j.neuron.2015.09.042","volume":"88","author":"S Wang","year":"2015","unstructured":"Wang S, Jiang M, Duchesne XMM, Laugeson EAA, Kennedy DPP, Adolphs R, and Zhao Q (2015) Atypical visual saliency in autism spectrum disorder quantified through model-based eye tracking. Neuron 88(3): 604\u2013616","journal-title":"Neuron"},{"issue":"4","key":"2032_CR47","first-page":"469","volume":"44","author":"J Mili\u00e8i\u00e6","year":"2003","unstructured":"Mili\u00e8i\u00e6 J, Petkovi\u00e6 BZ, Boikov J (2003) Dermatoglyphs of Digito-Palmar Complex in Autistic Disorder: Family Analysis. Clinical Sciences 44(4): 469\u2013476","journal-title":"Clinical Sciences"},{"key":"2032_CR48","unstructured":"Kazemi M, Bordbar MRF, Shahri NM (2017) Comparative Dermatoglyphic Study between Autistic Patients and Normal People in Iran. Iran J Med Sci 42(4)"},{"issue":"4","key":"2032_CR49","doi-asserted-by":"crossref","first-page":"386","DOI":"10.2298\/VSP1304386S","volume":"70","author":"M Sto\u0161ljevi\u00fc","year":"2013","unstructured":"Sto\u0161ljevi\u00fc M, and Adamovi\u00fc M, (2013) Dermatoglyphic characteristics of digito-palmar complex in autistic boys in Serbia. Vojnosanit Pregl 70(4): 386\u2013390","journal-title":"Vojnosanit Pregl"},{"issue":"8","key":"2032_CR50","doi-asserted-by":"crossref","first-page":"2150018","DOI":"10.1142\/S0129065721500180","volume":"31","author":"G Peng","year":"2021","unstructured":"Peng G, Nourani M, Harvey J, & Dave H, (2021) Personalized EEG Feature Selection for Low-Complexity Seizure Monitoring. International journal of neural systems 31(8): 2150018 (16 pages)","journal-title":"International journal of neural systems"},{"issue":"1","key":"2032_CR51","doi-asserted-by":"crossref","first-page":"3","DOI":"10.3233\/ICA-210664","volume":"29","author":"Y Xue","year":"2022","unstructured":"Xue Y, Zhu H, and Neri F (2022) A Self-adaptive Multi-objective Genetic Algorithm for Feature Selection in Classification. Integrated Computer-Aided Engineering 29(1): 3\u201321","journal-title":"Integrated Computer-Aided Engineering"},{"issue":"10","key":"2032_CR52","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1111\/mice.12840","volume":"37","author":"Y Tian","year":"2022","unstructured":"Tian Y, Ding X, Lin YF, Ma S, and Li L (2022) Automatic Feature Type Selection Network in Digital Photogrammetry of Piping. Computer-Aided Civil and Infrastructure Engineering 37(10): 1335\u20131348","journal-title":"Computer-Aided Civil and Infrastructure Engineering"},{"key":"2032_CR53","unstructured":"Ad-Dab\u2019bagh Y, Lyttelton O, Muehlboeck JS, et al (2006) The CIVET image processing environment: a fully automated comprehensive pipeline for anatomical neuroimaging research. In: Proceedings of the 12th Annual Meeting of the Organization for Human Brain Mapping Florence Italy 2266"},{"key":"2032_CR54","doi-asserted-by":"crossref","first-page":"11050","DOI":"10.1073\/pnas.200033797","volume":"97","author":"B Fischl","year":"2000","unstructured":"Fischl B, Dale AM (2000) Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci 97: 11050\u201311055","journal-title":"Proc Natl Acad Sci"},{"key":"2032_CR55","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1111\/pcn.12502","volume":"71","author":"N Yahata","year":"2017","unstructured":"Yahata N, Kasai K, and Kawato M (2017) Computational neuroscience approach to biomarkers and treatments for mental disorders. Psychiatry and Clinical Neurosciences 71: 215\u2013237","journal-title":"Psychiatry and Clinical Neurosciences"},{"key":"2032_CR56","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1006\/nimg.2000.0582","volume":"11","author":"J Ashburner","year":"2000","unstructured":"Ashburner J, and Friston KJ (2000) Voxel-based morphometry\u2014the techniques. NeuroImage 11: 805\u2013821","journal-title":"NeuroImage"},{"key":"2032_CR57","doi-asserted-by":"crossref","first-page":"e3631","DOI":"10.1371\/journal.pone.0003631","volume":"3","author":"RC Mandl","year":"2008","unstructured":"Mandl RC, Schnack HG, Zwiers MP, van der Schaaf A, Kahn RS, Pol HEH (2008) Functional diffusion tensor imaging: measuring task-related fractional anisotropy changes in the human brain along white matter tracts. PloS One 3 e3631","journal-title":"PloS One"},{"key":"2032_CR58","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1016\/j.neuroimage.2007.02.012","volume":"36","author":"Y Iturria-Medina","year":"2007","unstructured":"Iturria-Medina Y, Canales-Rodriguez EJ, Melie-Garcia L, Valdes-Hernandez PA, Martinez-Montes E, Alem\u00e1n-G\u00f3mez Y, S\u00e1nchez-Bornot JM (2007) Characterizing brain anatomical connections using diffusion weighted MRI and graph theory. NeuroImage 36: 645\u2013660","journal-title":"NeuroImage"},{"key":"2032_CR59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1089\/brain.2012.0122","volume":"3","author":"Y Iturria-Medina","year":"2013","unstructured":"Iturria-Medina Y (2013) Anatomical brain networks on the prediction of abnormal brain states. Brain connectivity 3: 1\u201321","journal-title":"Brain connectivity"},{"key":"2032_CR60","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1016\/j.neuroimage.2009.10.003","volume":"52","author":"M Rubinov","year":"2010","unstructured":"Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52: 1059\u20131069","journal-title":"NeuroImage"},{"key":"2032_CR61","doi-asserted-by":"publisher","first-page":"e08440","DOI":"10.7554\/eLife.08440","volume":"4","author":"Y Zeighami","year":"2015","unstructured":"Zeighami Y, Ulla M, Iturria-Medina Y, Dadar M, Zhang Y, Larcher KMH, Fonov V, Evans AC, Collins DL, Dagher A (2015) Network structure of brain atrophy in de novo Parkinson\u2019s disease. Elife 4 e08440. https:\/\/doi.org\/10.7554\/eLife.08440","journal-title":"Elife"},{"key":"2032_CR62","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.neuroimage.2008.11.007","volume":"45","author":"F Pereira","year":"2009","unstructured":"Pereira F, Mitchell T, Botvinick M (2009) Machine learning classifiers and fMRI: a tutorial overview. NeuroImage 45: 199\u2013209","journal-title":"NeuroImage"},{"issue":"8","key":"2032_CR63","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1515\/revneuro-2016-0052","volume":"27","author":"G Mirzaei","year":"2016","unstructured":"Mirzaei G, and Adeli H (2016) Resting State Functional Magnetic Resonance Image Processing Techniques in Stroke Studies. Reviews in the Neurosciences 27(8): 871\u2013885","journal-title":"Reviews in the Neurosciences"},{"key":"2032_CR64","doi-asserted-by":"crossref","unstructured":"Hassanpour A, Moradikia M, Adeli H, Khayami SR, and Shamsinejad P (2019) A Novel End-to-End Deep Learning Scheme For Classifying Multiclass Motor Imagery EEG Signals. Expert Systems 36(6).","DOI":"10.1111\/exsy.12494"},{"key":"2032_CR65","doi-asserted-by":"crossref","unstructured":"Gorriz JM, Ram\u00b4\u0131rez J, Segovia F, Mart\u00b4\u0131nez FJ, Lai MC, Lombardo MV, Baron-Cohen S, and Suckling J (2019) A Machine Learning Approach to Reveal the NeuroPhenotypes of Autisms. Int J Neural Sys 29(7)","DOI":"10.1142\/S0129065718500582"},{"key":"2032_CR66","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.nicl.2017.08.017","volume":"17","author":"AS Heinsfeld","year":"2018","unstructured":"Heinsfeld AS, Franco AR, Craddock C, Buchweitz A, & Meneguzzi F (2018) Identification of autism spectrum disorder using deep learning and the ABIDE dataset. NeuroImage: Clinical 17: 16\u201323. https:\/\/doi.org\/10.1016\/j.nicl.2017.08.017","journal-title":"NeuroImage: Clinical"},{"key":"2032_CR67","doi-asserted-by":"publisher","unstructured":"Li G, Liu M, Sun Q, Shen D, Wang L (2018) Early Diagnosis of Autism Disease by Multi-channel CNNs. Mach Learn Med Imaging 11046, 303\u2013309.\u00a0https:\/\/doi.org\/10.1007\/978-3-030-00919-9_35","DOI":"10.1007\/978-3-030-00919-9_35"},{"key":"2032_CR68","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1007\/s10278-018-0093-8","volume":"31","author":"MA Aghdam","year":"2018","unstructured":"Aghdam MA, Sharifi A, & Pedram MM (2018) Combination of rs-fMRI and sMRI Data to Discriminate Autism Spectrum Disorders in Young Children Using Deep Belief Network. Journal of Digital Imaging 31: 895\u2013903","journal-title":"Journal of Digital Imaging"},{"key":"2032_CR69","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.neucom.2018.04.080","volume":"324","author":"Y Kong","year":"2019","unstructured":"Kong Y, Gao J, Xu Y, Pan Y, Wang J, Liu J (2019) Classification of autism spectrum disorder by combining brain connectivity and deep neural network classifier. Neurocomputing 324: 63\u201368","journal-title":"Neurocomputing"},{"issue":"6","key":"2032_CR70","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1038\/mp.2013.78","volume":"19","author":"A Di Martino","year":"2014","unstructured":"Di Martino A, Yan CG, Li Q, Denio E, Castellanos FX, Alaerts K, Anderson JS, Assaf M, Bookheimer SY, Dapretto M et al. (2014) The Autism Brain Imaging Data Exchange: towards large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry 19(6): 659\u2013667 https:\/\/doi.org\/10.1038\/mp.2013.78","journal-title":"Molecular Psychiatry"},{"key":"2032_CR71","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1007\/s40998-021-00426-9","volume":"45","author":"E Irmak","year":"2021","unstructured":"Irmak E, (2021) Multi-Classification of Brain Tumor MRI Images Using Deep Convolutional Neural Network with Fully Optimized Framework. Iranian Journal of Science and Technology Transactions of Electrical Engineering 45:1015\u20131036","journal-title":"Iranian Journal of Science and Technology Transactions of Electrical Engineering"},{"key":"2032_CR72","doi-asserted-by":"crossref","unstructured":"Michelucci U (2019) Advanced Applied Deep Learning: Convolutional Neural Networks and Object Detection, 1st ed. Apress","DOI":"10.1007\/978-1-4842-4976-5_1"},{"key":"2032_CR73","volume-title":"Pattern Recognition and Machine Learning","author":"CM Bishop","year":"2006","unstructured":"Bishop CM (2006) Pattern Recognition and Machine Learning. Springer New York NY"},{"key":"2032_CR74","doi-asserted-by":"crossref","first-page":"197","DOI":"10.3233\/ICA-2010-0345","volume":"17","author":"M Ahmadlou","year":"2006","unstructured":"Ahmadlou M, & Adeli H (2006) Enhanced probabilistic neural network with local decision circles: A robust classifier. Integr Comput Aided Eng 17: 197\u2013210","journal-title":"Integr Comput Aided Eng"},{"issue":"12","key":"2032_CR75","doi-asserted-by":"publisher","first-page":"3074","DOI":"10.1109\/TNNLS.2017.2682102","volume":"28","author":"MH Rafiei","year":"2017","unstructured":"Rafiei MH and Adeli H (2017) A New Neural Dynamic Classification Algorithm,\u201d IEEE Transactions on Neural Networks and Learning Systems, 28(12): 3074\u20133083 https:\/\/doi.org\/10.1109\/TNNLS.2017.2682102","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"}],"container-title":["Journal of Medical Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-023-02032-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10916-023-02032-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-023-02032-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T02:31:30Z","timestamp":1736821890000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10916-023-02032-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,22]]},"references-count":75,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["2032"],"URL":"https:\/\/doi.org\/10.1007\/s10916-023-02032-0","relation":{},"ISSN":["1573-689X"],"issn-type":[{"value":"1573-689X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,22]]},"assertion":[{"value":"29 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"15"}}