{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:36:28Z","timestamp":1773246988229,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T00:00:00Z","timestamp":1683676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Axioms"],"abstract":"<jats:p>In this paper, a novel method for the automatic classification of coronary stenosis based on a feature selection strategy driven by a hybrid evolutionary algorithm is proposed. The main contribution is the characterization of the coronary stenosis anomaly based on the automatic selection of an efficient feature subset. The initial feature set consists of 49 features involving intensity, texture and morphology. Since the feature selection search space was O(2n), being n=49, it was treated as a high-dimensional combinatorial problem. For this reason, different single and hybrid evolutionary algorithms were compared, where the hybrid method based on the Boltzmann univariate marginal distribution algorithm (BUMDA) and simulated annealing (SA) achieved the best performance using a training set of X-ray coronary angiograms. Moreover, two different databases with 500 and 2700 stenosis images, respectively, were used for training and testing of the proposed method. In the experimental results, the proposed method for feature selection obtained a subset of 11 features, achieving a feature reduction rate of 77.5% and a classification accuracy of 0.96 using the training set. In the testing step, the proposed method was compared with different state-of-the-art classification methods in both databases, obtaining a classification accuracy and Jaccard coefficient of 0.90 and 0.81 in the first one, and 0.92 and 0.85 in the second one, respectively. In addition, based on the proposed method\u2019s execution time for testing images (0.02 s per image), it can be highly suitable for use as part of a clinical decision support system.<\/jats:p>","DOI":"10.3390\/axioms12050462","type":"journal-article","created":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T01:37:24Z","timestamp":1683769044000},"page":"462","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Automatic Classification of Coronary Stenosis Using Feature Selection and a Hybrid Evolutionary Algorithm"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4829-6854","authenticated-orcid":false,"given":"Miguel-Angel","family":"Gil-Rios","sequence":"first","affiliation":[{"name":"Departamento de Tecnolog\u00edas de la Informaci\u00f3n, Universidad Tecnol\u00f3gica de Le\u00f3n, Blvd. Universidad Tecnol\u00f3gica 225, Col. San Carlos, Le\u00f3n 37670, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9309-7531","authenticated-orcid":false,"given":"Claire","family":"Chalopin","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Health, University of Applied Sciences and Arts, 37085 G\u00f6ttingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5197-2059","authenticated-orcid":false,"given":"Ivan","family":"Cruz-Aceves","sequence":"additional","affiliation":[{"name":"CONACYT, Center for Research in Mathematics (CIMAT), A.C., Jalisco S\/N, Col. Valenciana, Guanajuato 36000, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6546-4357","authenticated-orcid":false,"given":"Juan-Manuel","family":"Lopez-Hernandez","sequence":"additional","affiliation":[{"name":"Divisi\u00f3n de Ingenier\u00edas (DICIS), Campus Irapuato-Salamanca, Universidad de Guanajuato, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 km, Comunidad de Palo Blanco, Salamanca 36885, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6903-2233","authenticated-orcid":false,"given":"Martha-Alicia","family":"Hernandez-Gonzalez","sequence":"additional","affiliation":[{"name":"Unidad M\u00e9dica de Alta Especialidad (UMAE), Hospital de Especialidades No. 1, Centro M\u00e9dico Nacional del Bajio, IMSS, Le\u00f3n 37320, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7501-6088","authenticated-orcid":false,"given":"Sergio-Eduardo","family":"Solorio-Meza","sequence":"additional","affiliation":[{"name":"Divisi\u00f3n Ciencias de la Salud, Campus Le\u00f3n, Universidad Tecnol\u00f3gica de M\u00e9xico, Blvd. Juan Alonso de Torres 1041, Le\u00f3n 37200, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1161\/CIR.0000000000001052","article-title":"Heart Disease and Stroke Statistics\u20142022 Update: A Report From the American Heart Association","volume":"145","author":"Tsao","year":"2022","journal-title":"Circulation"},{"key":"ref_2","first-page":"7381","article-title":"Segmentation of Coronary Artery Images and Detection of Atherosclerosis","volume":"13","author":"Saad","year":"2018","journal-title":"J. Eng. Appl. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1504\/IJBET.2019.102974","article-title":"Automatic stenosis grading system for diagnosing coronary artery disease using coronary angiogram","volume":"31","author":"Kishore","year":"2019","journal-title":"Int. J. Biomed. Eng. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.cmpb.2018.10.013","article-title":"Automated identification and grading of coronary artery stenoses with X-ray angiography","volume":"167","author":"Wan","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sameh, S., Azim, M.A., and AbdelRaouf, A. (2017, January 9\u201320). Narrowed Coronary Artery Detection and Classification using Angiographic Scans. Proceedings of the 2017 12th International Conference on Computer Engineering and Systems (ICCES), Cairo, Egypt.","DOI":"10.1109\/ICCES.2017.8275280"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"020005","DOI":"10.1063\/1.4954092","article-title":"Automatic detection of coronary artery stenosis in X-ray angiograms using Gaussian filters and genetic algorithms","volume":"1747","year":"2016","journal-title":"AIP Conf. Proc."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Cervantes-Sanchez, F., Cruz-Aceves, I., Hernandez-Aguirre, A., Hernandez-Gonzalez, M.A., and Solorio-Meza, S.E. (2019). Automatic Segmentation of Coronary Arteries in X-ray Angiograms using Multiscale Analysis and Artificial Neural Networks. MDPI Appl. Sci., 9.","DOI":"10.3390\/app9245507"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1007\/s11548-008-0235-4","article-title":"Automatic segmentation of calcified plaques and vessel borders in IVUS images","volume":"2008","author":"Taki","year":"2008","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.compmedimag.2015.03.003","article-title":"Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy","volume":"43","author":"Welikala","year":"2015","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sreng, S., Maneerat, N., Hamamoto, K., and Panjaphongse, R. (2018). Automated Diabetic Retinopathy Screening System Using Hybrid Simulated Annealing and Ensemble Bagging Classifier. Appl. Sci., 8.","DOI":"10.3390\/app8071198"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chen, X., Fu, Y., Lin, J., Ji, Y., Fang, Y., and Wu, J. (2020). Coronary Artery Disease Detection by Machine Learning with Coronary Bifurcation Features. Appl. Sci., 10.","DOI":"10.3390\/app10217656"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Giannoglou, V.G., Stavrakoudis, D.G., and Theocharis, J.B. (2012, January 11\u201313). IVUS-based characterization of atherosclerotic plaques using feature selection and SVM classification. Proceedings of the 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE), Larnaca, Cyprus.","DOI":"10.1109\/BIBE.2012.6399755"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2520706","DOI":"10.1155\/2018\/2520706","article-title":"Integrating Correlation-Based Feature Selection and Clustering for Improved Cardiovascular Disease Diagnosis","volume":"2018","author":"Wosiak","year":"2021","journal-title":"Complexity"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Gudigar, A., Nayak, S., Samanth, J., Raghavendra, U., A J, A., Barua, P.D., Hasan, M.N., Ciaccio, E.J., Tan, R.S., and Rajendra Acharya, U. (2021). Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph181910003"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.future.2018.03.023","article-title":"Automated system for the detection of thoracolumbar fractures using a CNN architecture","volume":"85","author":"Raghavendra","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.ins.2018.01.051","article-title":"Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images","volume":"441","author":"Raghavendra","year":"2018","journal-title":"Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"101059","DOI":"10.1016\/j.swevo.2022.101059","article-title":"Brain programming is immune to adversarial attacks: Towards accurate and robust image classification using symbolic learning","volume":"71","author":"Olague","year":"2022","journal-title":"Swarm Evol. Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"04001","DOI":"10.1051\/matecconf\/201821004001","article-title":"Stenosis Detection with Deep Convolutional Neural Networks","volume":"210","author":"Antczak","year":"2018","journal-title":"Proc. MATEC Web Conf."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ovalle-Magallanes, E., Avina-Cervantes, J.G., Cruz-Aceves, I., and Ruiz-Pinales, J. (2020). Transfer Learning for Stenosis Detection in X-ray Coronary Angiography. Mathematics, 8.","DOI":"10.3390\/math8091510"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Azizpour, H., Sharif Razavian, A., Sullivan, J., Maki, A., and Carlsson, S. (2015, January 7\u201312). From Generic to Specific Deep Representations for Visual Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301270"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4466","DOI":"10.1109\/ACCESS.2018.2885997","article-title":"CXNet-m1: Anomaly Detection on Chest X-rays with Image-Based Deep Learning","volume":"7","author":"Xu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1186\/s40537-019-0276-2","article-title":"Deep convolutional neural network based medical image classification for disease diagnosis","volume":"6","author":"Yadav","year":"2019","journal-title":"J. Big Data"},{"key":"ref_23","first-page":"364","article-title":"Convolutional Neural Network With Data Augmentation for SAR Target Recognition","volume":"13","author":"Ding","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1111\/1754-9485.13261","article-title":"A review of medical image data augmentation techniques for deep learning applications","volume":"65","author":"Chlap","year":"2021","journal-title":"Med. Imaging Radiat. Oncol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"106391","DOI":"10.1016\/j.compbiomed.2022.106391","article-title":"Data augmentation for medical imaging: A systematic literature review","volume":"152","author":"Garcea","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Goceri, E. (2023). Medical image data augmentation: Techniques, comparisons and interpretations. Artif. Intell. Rev.","DOI":"10.1007\/s10462-023-10453-z"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kebaili, A., Lapuyade-Lahorgue, J., and Ruan, S. (2023). Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review. J. Imaging, 9.","DOI":"10.3390\/jimaging9040081"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Knapi\u010d, S., Malhi, A., Saluja, R., and Fr\u00e4mling, K. (2021). Explainable Artificial Intelligence for Human Decision Support System in the Medical Domain. Mach. Learn. Knowl. Extr., 3.","DOI":"10.3390\/make3030037"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"102470","DOI":"10.1016\/j.media.2022.102470","article-title":"Explainable artificial intelligence (XAI) in deep learning-based medical image analysis","volume":"79","author":"Kuijf","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1162\/evco.2008.16.4.483","article-title":"Automated Design of Image Operators that Detect Interest Points","volume":"16","author":"Trujillo","year":"2008","journal-title":"Evol. Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1016\/j.neucom.2014.08.003","article-title":"A survey of recent advances in visual feature detection","volume":"149","author":"Li","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_32","first-page":"726002","article-title":"Multi-scale feature extraction for learning-based classification of coronary artery stenosis","volume":"Volume 7260","author":"Karssemeijer","year":"2009","journal-title":"Proceedings of the Medical Imaging 2009: Computer-Aided Diagnosis"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2566","DOI":"10.1016\/j.asoc.2012.03.058","article-title":"Interest point detection through multiobjective genetic programming","volume":"12","author":"Olague","year":"2012","journal-title":"Appl. Soft Comput."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Fazlali, H.R., Karimi, N., Soroushmehr, S.M.R., Sinha, S., Samavi, S., Nallamothu, B., and Najarian, K. (2015, January 27\u201330). Vessel region detection in coronary X-ray angiograms. Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), Quebec, QC, Canada.","DOI":"10.1109\/ICIP.2015.7351049"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1016\/j.ultrasmedbio.2012.01.015","article-title":"Atherosclerotic Risk Stratification Strategy for Carotid Arteries Using Texture-Based Features","volume":"38","author":"Acharya","year":"2022","journal-title":"Ultrasound Med. Biol."},{"key":"ref_36","first-page":"4206","article-title":"Texture Analysis based on the Gray-Level Co-Ocurrence Matrix considering possible orientations","volume":"2","author":"Pathak","year":"2013","journal-title":"Int. J. Adv. Res. Electr. Electron. Instrum. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ejmp.2016.12.005","article-title":"Computer aided diagnosis of Coronary Artery Disease, Myocardial Infarction and carotid atherosclerosis using ultrasound images: A review","volume":"33","author":"Faust","year":"2017","journal-title":"Phys. Medica"},{"key":"ref_38","first-page":"912","article-title":"Retinal vascular tree morphology: A semi-automatic quantification","volume":"8","author":"Mitchell","year":"2019","journal-title":"J. Am. Heart Assoc."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2181","DOI":"10.1177\/1460458219899210","article-title":"Linear discriminant analysis and principal component analysis to predict coronary artery disease","volume":"26","author":"Ricciardi","year":"2020","journal-title":"Health Inform. J."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.cviu.2006.08.012","article-title":"Visual learning of texture descriptors for facial expression recognition in thermal imagery","volume":"106","author":"Olague","year":"2007","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Barburiceanu, S., Terebes, R., and Meza, S. (2021). 3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors. Appl. Sci., 11.","DOI":"10.3390\/app11052332"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Cheng, K., Lin, A., Yuvaraj, J., Nicholls, S.J., and Wong, D.T. (2021). Cardiac Computed Tomography Radiomics for the Non-Invasive Assessment of Coronary Inflammation. Cells, 10.","DOI":"10.3390\/cells10040879"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ayx, I., Tharmaseelan, H., Hertel, A., N\u00f6renberg, D., Overhoff, D., Rotkopf, L.T., Riffel, P., Schoenberg, S.O., and Froelich, M.F. (2022). Myocardial Radiomics Texture Features Associated with Increased Coronary Calcium Score-First Results of a Photon-Counting CT. Diagnostics, 12.","DOI":"10.1038\/s41598-022-22877-8"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/0167-8655(86)90009-7","article-title":"Linear feature detection and enhancement in noisy images via the Radon transform","volume":"4","author":"Murphy","year":"1986","journal-title":"Pattern Recognit. Lett."},{"key":"ref_45","unstructured":"Mallat, S. (2009). A Wavelet Tour of Signal Processing, Elsevier. [3rd ed.]. Chapter 13."},{"key":"ref_46","unstructured":"Timothy-G, F. (2015). The Mathematics of Medical Imaging, Technical University of Denmark."},{"key":"ref_47","unstructured":"Frangi, A., Nielsen, W., Vincken, K., and Viergever, M. (1998). Medical Image Computing and Computer-Assisted Intervention (MICCAI\u201998), Springer."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A Threshold Selection Method from Gray-Level Histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1126\/science.220.4598.671","article-title":"Optimization by simulated annealing","volume":"220","author":"Kirkpatrick","year":"1983","journal-title":"Science"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.ins.2013.02.040","article-title":"A Boltzmann based estimation of distribution algorithm","volume":"236","author":"Botello","year":"2013","journal-title":"Inf. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1002\/etep.1854","article-title":"A hybrid Univariate Marginal Distribution Algorithm for dynamic economic dispatch of units considering valve-point effects and ramp rates","volume":"25","author":"Gu","year":"2015","journal-title":"Int. Trans. Electr. Energy Syst."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1007\/s00453-018-0507-5","article-title":"Level-Based Analysis of the Univariate Marginal Distribution Algorithm","volume":"81","author":"Dang","year":"2017","journal-title":"Algorithmica"},{"key":"ref_53","unstructured":"Hashemi, M., and Reza-Meybodi, M. (2011). International Conference on Neural Information Processing, Springer."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Cristianini, N., and Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press.","DOI":"10.1017\/CBO9780511801389"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1038\/nbt1206-1565","article-title":"What is a support vector machine?","volume":"24","author":"Noble","year":"2006","journal-title":"Nat. Biotechnol."},{"key":"ref_57","unstructured":"Tong, S., and Chang, E. Support Vector Machine Active Learning for Image Retrieval. Proceedings of the Ninth ACM International Conference on Multimedia."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","article-title":"A Tutorial on Support Vector Machines for Pattern Recognition","volume":"2","author":"Burges","year":"1998","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/0022-0000(88)90046-3","article-title":"How easy is Local Search?","volume":"37","author":"Johnson","year":"1988","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_61","first-page":"129","article-title":"Automatic enhancement of coronary arteries using convolutional gray-level templates and path-based metaheuristics","volume":"1","author":"Guryev","year":"2021","journal-title":"Recent Trends Comput. Intell. Enabled Res."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2014). Going Deeper with Convolutions. arXiv.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Harouni, A., Karargyris, A., Negahdar, M., Beymer, D., and Syeda-Mahmood, T. (2018, January 4\u20137). Universal multi-modal deep network for classification and segmentation of medical images. Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA.","DOI":"10.1109\/ISBI.2018.8363710"}],"container-title":["Axioms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-1680\/12\/5\/462\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:32:09Z","timestamp":1760124729000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-1680\/12\/5\/462"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,10]]},"references-count":63,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["axioms12050462"],"URL":"https:\/\/doi.org\/10.3390\/axioms12050462","relation":{},"ISSN":["2075-1680"],"issn-type":[{"value":"2075-1680","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,10]]}}}