{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T18:37:05Z","timestamp":1774895825464,"version":"3.50.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2023,12,29]],"date-time":"2023-12-29T00:00:00Z","timestamp":1703808000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,12,29]],"date-time":"2023-12-29T00:00:00Z","timestamp":1703808000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Egyptian Russian University"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Coronavirus disease (COVID-19), impacted by SARS-CoV-2, is one of the greatest challenges of the twenty-first century. COVID-19 broke out in the world over the last 2\u00a0years and has caused many injuries and killed persons. Computer-aided diagnosis has become a necessary tool to prevent the spreading of this virus. Detecting COVID-19 at an early stage is essential to reduce the mortality risk of patients. Researchers seek to find rapid solutions based on techniques of Machine Learning and Deep Learning. In this paper, we introduced a hybrid model for COVID-19 detection based on machine learning and deep learning models. We used 10 different deep CNN network models to extract features from CT images. We extract features from different layers in each network and find the optimum layer that gives the best-extracted features for each CNN network. Then, for classifying these features, we used five different classifiers based on machine learning. The dataset consists of 2481 CT images divided into COVID-19 and non-COVID-19 categories. Three folds are extracted with a different size between testing and training. Through experiments, we define the best layer for all used CNN networks, the best network, and the best-used classifier. The measured performance shows the superiority of the proposed system over the literature with a highest accuracy of 99.39%. Our models are tested with the three folds that gained maximum average accuracy. The result is 98.69%.<\/jats:p>","DOI":"10.1007\/s00521-023-09346-7","type":"journal-article","created":{"date-parts":[[2023,12,29]],"date-time":"2023-12-29T05:02:45Z","timestamp":1703826165000},"page":"5347-5365","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["COVID-19 classification based on a deep learning and machine learning fusion technique using chest CT images"],"prefix":"10.1007","volume":"36","author":[{"given":"Gerges M.","family":"Salama","sequence":"first","affiliation":[]},{"given":"Asmaa","family":"Mohamed","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6840-2503","authenticated-orcid":false,"given":"Mahmoud Khaled","family":"Abd-Ellah","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,29]]},"reference":[{"key":"9346_CR1","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.655","author":"H Alshazly","year":"2021","unstructured":"Alshazly H, Linse C, Abdalla M, Barth E, Martinetz T (2021) COVID-Nets: deep CNN architectures for detecting COVID-19 using chest CT scans. Peer J Comput sci. https:\/\/doi.org\/10.7717\/peerj-cs.655","journal-title":"Peer J Comput sci"},{"key":"9346_CR2","doi-asserted-by":"publisher","first-page":"112455","DOI":"10.1016\/j.bios.2020.112455","volume":"166","author":"T Ji","year":"2020","unstructured":"Ji T, Liu Z, Wang G, Guo X, Akbar Khan S, Lai C, Chen H, Huang S, Xia S, Chen B, Jia H, Chen Y, Zhou Q (2020) Detection of COVID-19: A review of the current literature and future perspectives. Biosens Bioelectron 166:112455","journal-title":"Biosens Bioelectron"},{"key":"9346_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-822548-6.00080-7","author":"G Rong","year":"2021","unstructured":"Rong G, Zheng Y, Chen Y, Zhang Y, Zhu P, Sawan M (2021) COVID-19 diagnostic methods and detection techniques: a review. Ref Module Biomed Sci. https:\/\/doi.org\/10.1016\/B978-0-12-822548-6.00080-7","journal-title":"Ref Module Biomed Sci"},{"key":"9346_CR4","doi-asserted-by":"publisher","first-page":"6601","DOI":"10.3390\/en13246601","volume":"13","author":"D Skrobek","year":"2020","unstructured":"Skrobek D, Krzywanski J, Sosnowski M, Ku\u0142akowska A, Zylka A, Grabowska K, Ciesielska K, Nowak W (2020) Prediction of sorption processes using the deep learning methods (long short-term memory). Energies 13:6601","journal-title":"Energies"},{"issue":"1","key":"9346_CR5","doi-asserted-by":"publisher","first-page":"04015017","DOI":"10.1061\/(ASCE)EY.1943-7897.0000280","volume":"142","author":"J Krzywanski","year":"2014","unstructured":"Krzywanski J, Blaszczuk A, Czakiert T, Rajczyk R, Nowak W (2014) Artificial intelligence treatment of NOx emissions from CFBC in air and oxy-fuel conditions. J Energy Eng 142(1):04015017","journal-title":"J Energy Eng"},{"issue":"21","key":"9346_CR6","doi-asserted-by":"publisher","first-page":"5619","DOI":"10.3390\/en13215619","volume":"13","author":"W Muhammad Ashraf","year":"2020","unstructured":"Muhammad Ashraf W, Moeen Uddin G, Hassan Kamal A, Haider Khan M, Khan AA, Afroze Ahmad H, Ahmed F, Hafeez N, Muhammad Zawar Sami R, Muhammad Arafat S, Gul Niazi S, Waqas Rafique M, Amjad A, Hussain J, Jamil H, Kathia MS, Krzywanski J (2020) Optimization of a 660 MWe supercritical power plant performance\u2014a case of industry 4.0 in the data-driven operational management. part 2. power generation. Energies 13(21):5619","journal-title":"Energies"},{"issue":"3","key":"9346_CR7","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1007\/s12525-021-00475-2","volume":"31","author":"C Janiesch","year":"2021","unstructured":"Janiesch C, Zschech P, Heinrich K (2021) Machine learning and deep learning. Electron Markets 31(3):685\u2013695","journal-title":"Electron Markets"},{"key":"9346_CR8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32644-9_36","author":"A Ghosh","year":"2020","unstructured":"Ghosh A, Sufian A, Sultana F, Chakrabarti A, De D (2020) Fundamental concepts of convolutional neural network. Recent trends adv artif intell Internet Things. https:\/\/doi.org\/10.1007\/978-3-030-32644-9_36","journal-title":"Recent trends adv artif intell Internet Things"},{"key":"9346_CR9","unstructured":"O'Shea V, Nash R (2015) An introduction to convolutional neural networks. ArXiv e-prints, 11\/01"},{"key":"9346_CR10","first-page":"37","volume":"4","author":"H Kasban","year":"2015","unstructured":"Kasban H, El-bendary M, Salama D (2015) A comparative study of medical imaging techniques. Int J Inf Sci Intell Syst 4:37\u201358","journal-title":"Int J Inf Sci Intell Syst"},{"key":"9346_CR11","first-page":"504","volume-title":"Medical imaging: a review","author":"D Ganguly","year":"2010","unstructured":"Ganguly D, Chakraborty S, Balitanas M, Kim T-H (2010) Medical imaging: a review. Springer, Berlin, pp 504\u2013516"},{"key":"9346_CR12","doi-asserted-by":"crossref","unstructured":"Miranda E, Aryuni M, Irwansyah E (2016) A Survey of Medical Image Classification Techniques. In: 2016 international conference on information management and technology (ICIMTech). IEEE","DOI":"10.1109\/ICIMTech.2016.7930302"},{"key":"9346_CR13","doi-asserted-by":"publisher","first-page":"2936","DOI":"10.30534\/ijatcse\/2021\/021052021","volume":"10","author":"F Yimer","year":"2021","unstructured":"Yimer F, Tessema A, Simegn G (2021) Multiple lung diseases classification from chest x-ray images using deep learning approach. Int J Adv Trends Comput Sci Eng 10:2936\u20132946","journal-title":"Int J Adv Trends Comput Sci Eng"},{"key":"9346_CR14","doi-asserted-by":"publisher","first-page":"900","DOI":"10.14741\/ijcet\/v.10.6.1","volume":"10","author":"TA Sadoon","year":"2020","unstructured":"Sadoon TA, Ali M (2020) An overview of medical images classification based on CNN. Int J Curr Eng Technol 10:900\u2013905","journal-title":"Int J Curr Eng Technol"},{"key":"9346_CR15","unstructured":"Rushnaiwala T (2021) XCeption model"},{"key":"9346_CR16","unstructured":"Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767"},{"key":"9346_CR17","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"9346_CR18","unstructured":"Krizhevsky A (2014) One weird trick for parallelizing convolutional neural networks. arXiv preprint arXiv:1404.5997"},{"key":"9346_CR19","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"9346_CR20","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L.-C (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"9346_CR21","unstructured":"Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) SqueezeNet: alexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size, arXiv preprint arXiv:1602.07360"},{"key":"9346_CR22","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A (2017) YOLO9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7263-7271","DOI":"10.1109\/CVPR.2017.690"},{"key":"9346_CR23","unstructured":"Al-Saffar B, Abdulmajeed NK COVID-19 Pandemic Detection in Chest X-ray Images by Deep Features with SVM Classifier"},{"key":"9346_CR24","unstructured":"Hemdan EED, Shouman MA, Karar ME (2020) COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images. ArXiv, vol. abs\/2003.11055"},{"key":"9346_CR25","doi-asserted-by":"crossref","unstructured":"Sethy P, Santi K, Behera, Kumar P, Biswas P (2020) Detection of coronavirus Disease (COVID-19) based on Deep Features and Support Vector Machine, pp 643\u2013651","DOI":"10.33889\/IJMEMS.2020.5.4.052"},{"key":"9346_CR26","doi-asserted-by":"crossref","unstructured":"Fernandez\u2013Grandon C, Soto I, Zabala-Blanco D, Alavia W, Garcia V (2021) SVM and ANN classification using GLCM and HOG features for COVID-19 and Pneumonia detection from Chest X-rays. In: 2021 Third South American Colloquium on Visible Light Communications (SACVLC). IEEE, pp 01\u201306","DOI":"10.1109\/SACVLC53127.2021.9652248"},{"key":"9346_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114054","volume":"164","author":"AM Ismael","year":"2021","unstructured":"Ismael AM, \u015eeng\u00fcr A (2021) Deep learning approaches for COVID-19 detection based on chest X-ray images. Expert Syst Appl 164:114054","journal-title":"Expert Syst Appl"},{"key":"9346_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2020.106960","volume":"90","author":"MA Khan","year":"2021","unstructured":"Khan MA, Kadry S, Zhang Y-D, Akram T, Sharif M, Rehman A, Saba T (2021) Prediction of COVID-19 - pneumonia based on selected deep features and one class kernel extreme learning machine. Comput elect eng int j 90:106960","journal-title":"Comput elect eng int j"},{"issue":"09","key":"9346_CR29","doi-asserted-by":"publisher","first-page":"299","DOI":"10.55525\/tjst.1092676","volume":"08","author":"N Aslan","year":"2022","unstructured":"Aslan N, Dogan S, Ozmen Koca G (2022) Classification of chest X-ray COVID-19 images using the local binary pattern feature extraction method. Turk J Sci Technol 08(09):299\u2013308","journal-title":"Turk J Sci Technol"},{"key":"9346_CR30","first-page":"105045","volume":"31","author":"JN Hasoon","year":"2021","unstructured":"Hasoon JN, Fadel AH, Hameed RS, Mostafa SA, Khalaf BA, Mohammed MA, Nedoma J (2021) COVID-19 anomaly detection and classification method based on supervised machine learning of chest X-ray images. Res Phys 31:105045","journal-title":"Res Phys"},{"key":"9346_CR31","doi-asserted-by":"publisher","first-page":"e553","DOI":"10.7717\/peerj-cs.553","volume":"7","author":"RJ Al-Azawi","year":"2021","unstructured":"Al-Azawi RJ, Al-Saidi NMG, Jalab HA, Kahtan H, Ibrahim RW (2021) Efficient classification of COVID-19 CT scans by using q-transform model for feature extraction. Peer J Comput Sci 7:e553","journal-title":"Peer J Comput Sci"},{"key":"9346_CR32","doi-asserted-by":"publisher","first-page":"7672196","DOI":"10.1155\/2022\/7672196","volume":"2022","author":"SV Kogilavani","year":"2022","unstructured":"Kogilavani SV, Prabhu J, Sandhiya R, Kumar MS, Subramaniam U, Karthick A, Muhibbullah M, Imam SBS (2022) COVID-19 detection based on lung ct scan using deep learning techniques. Comput Math Methods Med 2022:7672196","journal-title":"Comput Math Methods Med"},{"key":"9346_CR33","doi-asserted-by":"crossref","unstructured":"Sharma S, Tiwari S (2021) COVID-19 Diagnosis using X-Ray Images and Deep learning. In: 2021 International conference on artificial intelligence and smart systems (ICAIS), pp. 344\u2013349","DOI":"10.1109\/ICAIS50930.2021.9395851"},{"key":"9346_CR34","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.inffus.2021.02.013","volume":"72","author":"G Muhammad","year":"2021","unstructured":"Muhammad G, Shamim Hossain M (2021) COVID-19 and non-COVID-19 classification using multi-layers fusion from lung ultrasound images. Inf Fusion 72:80\u201388","journal-title":"Inf Fusion"},{"key":"9346_CR35","doi-asserted-by":"crossref","unstructured":"Rahimzadeh M, Attar A (2020) A new modified deep convolutional neural network for detecting covid-19 from x-ray images","DOI":"10.1016\/j.imu.2020.100360"},{"key":"9346_CR36","unstructured":"Yang S, Xiao W, Zhang M, Guo S, Zhao J, Shen F (2022) Image data augmentation for deep learning: a survey"},{"key":"9346_CR37","doi-asserted-by":"crossref","unstructured":"Alsaffar A, Tao H, Talab M (2017) Review of deep convolution neural network in image classification. In: 2017 International conference on radar, antenna, microwave, electronics, and telecommunications (ICRAMET). IEEE","DOI":"10.1109\/ICRAMET.2017.8253139"},{"key":"9346_CR38","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1007\/978-3-540-77058-9_36","volume-title":"Support vector machine classification for object-based image analysis","author":"A Tzotsos","year":"2008","unstructured":"Tzotsos A, Argialas D (2008) Support vector machine classification for object-based image analysis. Springer, Berlin, pp 663\u2013677"},{"key":"9346_CR39","volume-title":"Efficient learning machines: theories, concepts, and applications for engineers and system designers","author":"R Khanna","year":"2015","unstructured":"Khanna R, Awad M (2015) Efficient learning machines: theories, concepts, and applications for engineers and system designers. Springer, Berlin"},{"key":"9346_CR40","first-page":"1","volume":"36","author":"S Suthaharan","year":"2016","unstructured":"Suthaharan S (2016) machine learning models and algorithms for big data classification: thinking with examples for effective learning. Integr Ser Inf Syst 36:1\u20132","journal-title":"Integr Ser Inf Syst"},{"key":"9346_CR41","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1613\/jair.105","volume":"2","author":"TG Dietterich","year":"1994","unstructured":"Dietterich TG, Bakiri G (1994) Solving multiclass learning problems via error-correcting output codes. J artif intell res 2:263\u2013286","journal-title":"J artif intell res"},{"key":"9346_CR42","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1186\/s13640-018-0332-4","volume":"2018","author":"M Abd-Ellah","year":"2018","unstructured":"Abd-Ellah M, Awad AI, Khalaf AAM, Hamed H (2018) Two-phase multi-model automatic brain tumor diagnosis system from magnetic resonance images using convolutional neural networks. EURASIP J Image Video Process 2018:97","journal-title":"EURASIP J Image Video Process"},{"key":"9346_CR43","doi-asserted-by":"crossref","unstructured":"Taunk K, De S, Verma S, Swetapadma A (2019) A Brief Review of Nearest Neighbor Algorithm for Learning and Classification. In: 2019 international conference on intelligent computing and control systems (ICCS). IEEE","DOI":"10.1109\/ICCS45141.2019.9065747"},{"key":"9346_CR44","doi-asserted-by":"crossref","unstructured":"Min-Ling Z, Zhi-Hua Z (2005) A k-nearest neighbor based algorithm for multi-label classification. In: 2005 IEEE international conference on granular computing, pp. 718\u2013721 Vol. 2","DOI":"10.1109\/GRC.2005.1547385"},{"key":"9346_CR45","first-page":"1","volume-title":"Ensemble methods in machine learning","author":"TG Dietterich","year":"2000","unstructured":"Dietterich TG (2000) Ensemble methods in machine learning. Springer, Berlin, pp 1\u201315"},{"key":"9346_CR46","unstructured":"Ponti M (2011) Combining Classifiers: From the Creation of Ensembles to the Decision Fusion, In: 2011 24th SIBGRAPI conference on graphics, patterns, and images tutorials. IEEE"},{"key":"9346_CR47","first-page":"165","volume-title":"Decision trees","author":"L Rokach","year":"2005","unstructured":"Rokach L, Maimon O (2005) Decision trees. Springer, Berlin, pp 165\u2013192"},{"key":"9346_CR48","doi-asserted-by":"publisher","first-page":"20","DOI":"10.38094\/jastt20165","volume":"2","author":"B Jijo","year":"2021","unstructured":"Jijo B, Mohsin Abdulazeez A (2021) Classification based on decision tree algorithm for machine learning. J Appl Sci Technol Trends 2:20\u201328","journal-title":"J Appl Sci Technol Trends"},{"key":"9346_CR49","doi-asserted-by":"publisher","first-page":"115127","DOI":"10.1016\/j.psychres.2023.115127","volume":"322","author":"D Colledani","year":"2023","unstructured":"Colledani D, Anselmi P, Robusto E (2023) Machine learning-decision tree classifiers in psychiatric assessment: an application to the diagnosis of major depressive disorder. Psychiatry Res 322:115127","journal-title":"Psychiatry Res"},{"key":"9346_CR50","doi-asserted-by":"crossref","unstructured":"Liu H, Cocea M, Ding W (2017) Decision tree learning based feature evaluation and selection for image classification. In: 2017 International conference on machine learning and cybernetics (ICMLC) pp. 569\u2013574","DOI":"10.1109\/ICMLC.2017.8108975"},{"key":"9346_CR51","doi-asserted-by":"publisher","first-page":"91","DOI":"10.3991\/ijes.v7i2.10659","volume":"7","author":"A Wibawa","year":"2019","unstructured":"Wibawa A, Kurniawan A, Murti D, Adiperkasa RP, Putra S, Kurniawan S, Nugraha Y (2019) Na\u00efve bayes classifier for journal quartile classification. Int J Recent Contrib Eng Sci IT (iJES) 7:91","journal-title":"Int J Recent Contrib Eng Sci IT (iJES)"},{"key":"9346_CR52","unstructured":"Soares E, Angelov P, Biaso S, Froes MH, Abe DK (2020) SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification. In: medRxiv, pp 2020.04.24.20078584"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09346-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09346-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09346-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T21:57:53Z","timestamp":1709935073000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09346-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,29]]},"references-count":52,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["9346"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09346-7","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,29]]},"assertion":[{"value":"14 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article\u2019s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit .","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Open access"}}]}}