{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T05:40:56Z","timestamp":1773466856760,"version":"3.50.1"},"reference-count":75,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,6,13]],"date-time":"2021-06-13T00:00:00Z","timestamp":1623542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007414","name":"Qassim University","doi-asserted-by":"publisher","award":["25650"],"award-info":[{"award-number":["25650"]}],"id":[{"id":"10.13039\/501100007414","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Since January 2020, the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected the whole world, producing a respiratory disease that can become severe and even cause death in certain groups of people. The main method for diagnosing coronavirus disease 2019 (COVID-19) is performing viral tests. However, the kits for carrying out these tests are scarce in certain regions of the world. Lung conditions as perceived in computed tomography and radiography images exhibit a high correlation with the presence of COVID-19 infections. This work attempted to assess the feasibility of using convolutional neural networks for the analysis of pulmonary radiography images to distinguish COVID-19 infections from non-infected cases and other types of viral or bacterial pulmonary conditions. The results obtained indicate that these networks can successfully distinguish the pulmonary radiographies of COVID-19-infected patients from radiographies that exhibit other or no pathology, with a sensitivity of 100% and specificity of 97.6%. This could help future efforts to automate the process of identifying lung radiography images of suspicious cases, thereby supporting medical personnel when many patients need to be rapidly checked. The automated analysis of pulmonary radiography is not intended to be a substitute for formal viral tests or formal diagnosis by a properly trained physician but rather to assist with identification when the need arises.<\/jats:p>","DOI":"10.3390\/a14060183","type":"journal-article","created":{"date-parts":[[2021,6,14]],"date-time":"2021-06-14T10:30:25Z","timestamp":1623666625000},"page":"183","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["The Practicality of Deep Learning Algorithms in COVID-19 Detection: Application to Chest X-ray Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2747-528X","authenticated-orcid":false,"given":"Abdulaziz","family":"Alorf","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, College of Engineering, Qassim University, Buraydah 51452, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,13]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (WHO) (2020, November 15). Coronavirus Disease (COVID-19) Pandemic. Available online: https:\/\/www.who.int\/emergencies\/diseases\/novel-coronavirus-2019."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/j.jceh.2020.04.020","article-title":"Sustainability of coronavirus on different surfaces","volume":"10","author":"Suman","year":"2020","journal-title":"J. Clin. Exp. Hepatol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wang, J., Tang, K., Feng, K., Lin, X., Lv, W., Chen, K., and Wang, F. (2020). High temperature and high humidity reduce the transmission of COVID-19. arXiv.","DOI":"10.2139\/ssrn.3551767"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e2011834","DOI":"10.1001\/jamanetworkopen.2020.11834","article-title":"Temperature, humidity, and latitude analysis to estimate potential spread and seasonality of coronavirus disease 2019 (COVID-19)","volume":"3","author":"Sajadi","year":"2020","journal-title":"JAMA Netw."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1406","DOI":"10.1001\/jama.2020.2565","article-title":"Presumed asymptomatic carrier transmission of COVID-19","volume":"323","author":"Bai","year":"2020","journal-title":"J. Am. Med. Assoc. (JAMA)"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1038\/s41569-020-0360-5","article-title":"COVID-19 and the cardiovascular system","volume":"17","author":"Zheng","year":"2020","journal-title":"Nat. Rev. Cardiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"105932","DOI":"10.1016\/j.ijantimicag.2020.105932","article-title":"Chloroquine and hydroxychloroquine as available weapons to fight COVID-19","volume":"55","author":"Colson","year":"2020","journal-title":"Int. J. Antimicrob. Agents"},{"key":"ref_8","first-page":"e7560","article-title":"A comprehensive literature review on the clinical presentation, and management of the pandemic coronavirus disease 2019 (COVID-19)","volume":"12","author":"Kakodkar","year":"2020","journal-title":"Cureus"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Batagelj, B., Peer, P., \u0160truc, V., and Dobri\u0161ek, S. (2021). How to correctly detect face-masks for COVID-19 from visual information?. Appl. Sci., 11.","DOI":"10.3390\/app11052070"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Qin, B., and Li, D. (2020). Identifying facemask-wearing condition using image super-resolution with classification network to prevent COVID-19. Sensors, 20.","DOI":"10.21203\/rs.3.rs-28668\/v1"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Taylor, W., Abbasi, Q.H., Dashtipour, K., Ansari, S., Shah, S.A., Khalid, A., and Imran, M.A. (2020). A review of the state of the art in non-contact sensing for COVID-19. Sensors, 20.","DOI":"10.3390\/s20195665"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Duran-Lopez, L., Dominguez-Morales, J.P., Corral-Jaime, J., Vicente-Diaz, S., and Linares-Barranco, A. (2020). COVID-XNet: A custom deep learning system to diagnose and locate COVID-19 in chest X-ray images. Appl. Sci., 10.","DOI":"10.3390\/app10165683"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Bourouis, S., Alharbi, A., and Bouguila, N. (2021). Bayesian learning of shifted-scaled Dirichlet mixture models and its application to early COVID-19 detection in chest X-ray images. J. Imaging, 7.","DOI":"10.3390\/jimaging7010007"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Alam, N.A., Ahsan, M., Based, M.A., Haider, J., and Kowalski, M. (2021). COVID-19 detection from chest X-ray images using feature fusion and deep learning. Sensors, 21.","DOI":"10.3390\/s21041480"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mazzilli, A., Fiorino, C., Loria, A., Mori, M., Esposito, P.G., Palumbo, D., de Cobelli, F., and del Vecchio, A. (2021). An automatic approach for individual HU-based characterization of lungs in COVID-19 patients. Appl. Sci., 11.","DOI":"10.3390\/app11031238"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Alshazly, H., Linse, C., Barth, E., and Martinetz, T. (2021). Explainable COVID-19 detection using chest CT scans and deep learning. Sensors, 21.","DOI":"10.3390\/s21020455"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zisad, S.N., Hossain, M.S., Hossain, M.S., and Andersson, K. (2021). An integrated neural network and SEIR model to predict COVID-19. Algorithms, 14.","DOI":"10.3390\/a14030094"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"AbdelAziz, A.M., Alarabi, L., Basalamah, S., and Hendawi, A. (2021). A multi-objective optimization method for hospital admission problem\u2014A case study on COVID-19 patients. Algorithms, 14.","DOI":"10.3390\/a14020038"},{"key":"ref_19","first-page":"1843","article-title":"Detection of SARS-CoV-2 in different types of clinical specimens","volume":"323","author":"Wang","year":"2020","journal-title":"J. Am. Med. Assoc. (JAMA)"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2000045","DOI":"10.2807\/1560-7917.ES.2020.25.3.2000045","article-title":"Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR","volume":"25","author":"Corman","year":"2020","journal-title":"Eurosurveillance"},{"key":"ref_21","first-page":"m1052","article-title":"Bearing the brunt of COVID-19: Older people in low and middle income countries","volume":"368","author":"Ebrahim","year":"2020","journal-title":"Br. Med. J. (BMJ)"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"87","DOI":"10.2214\/AJR.20.23034","article-title":"Coronavirus disease 2019 (COVID-19): A systematic review of imaging findings in 919 patients","volume":"215","author":"Salehi","year":"2020","journal-title":"Am. J. Roentgenol. (AJR)"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1148\/radiol.2020200230","article-title":"CT imaging features of 2019 novel coronavirus (2019-nCoV)","volume":"295","author":"Chung","year":"2020","journal-title":"Radiology"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1148\/radiol.2020200241","article-title":"Chest CT findings in 2019 novel coronavirus (2019-nCoV) infections from Wuhan, China: Key points for the radiologist","volume":"295","author":"Kanne","year":"2020","journal-title":"Radiology"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"E32","DOI":"10.1148\/radiol.2020200642","article-title":"Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: A report of 1014 cases","volume":"296","author":"Ai","year":"2020","journal-title":"Radiology"},{"key":"ref_26","unstructured":"GitHub (2020, November 15). Figure 1 COVID-19 Chest X-ray Dataset Initiative. Available online: https:\/\/github.com\/agchung\/Figure1-COVID-chestxray-dataset."},{"key":"ref_27","unstructured":"Yang, X., He, X., Zhao, J., Zhang, Y., Zhang, S., and Xie, P. (2020). COVID-CT-Dataset: A CT scan dataset about COVID-19. arXiv."},{"key":"ref_28","unstructured":"Cohen, J.P., Morrison, P., and Dao, L. (2020). COVID-19 image data collection. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Cohen, J.P., Morrison, P., Dao, L., Roth, K., Duong, T.Q., and Ghassemi, M. (2020). COVID-19 image data collection: Prospective predictions are the future. arXiv.","DOI":"10.59275\/j.melba.2020-48g7"},{"key":"ref_30","unstructured":"Kalkreuth, R., and Kaufmann, P. (2020). COVID-19: A survey on public medical imaging data resources. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/RBME.2020.2987975","article-title":"Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19","volume":"14","author":"Shi","year":"2020","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Guo, Q., Li, M., Wang, C., Wang, P., Fang, Z., Tan, J., Wu, S., Xiao, Y., and Zhu, H. (2020). Host and infectivity prediction of Wuhan 2019 novel coronavirus using deep learning algorithm. bioRxiv.","DOI":"10.1101\/2020.01.21.914044"},{"key":"ref_33","first-page":"132","article-title":"Finding an accurate early forecasting model from small dataset: A case of 2019-nCoV novel coronavirus outbreak","volume":"6","author":"Dey","year":"2020","journal-title":"Int. J. Interact. Multimed. Artif. Intell. (IJIMAI)"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.eng.2020.04.010","article-title":"A deep learning system to screen novel coronavirus disease 2019 pneumonia","volume":"6","author":"Xu","year":"2020","journal-title":"Engineering"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"19196","DOI":"10.1038\/s41598-020-76282-0","article-title":"Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: A prospective study","volume":"10","author":"Chen","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_36","unstructured":"Gozes, O., Frid-Adar, M., Greenspan, H., Browning, P.D., Zhang, H., Ji, W., Bernheim, A., and Siegel, E. (2020). Rapid AI development cycle for the coronavirus (COVID-19) pandemic: Initial results for automated detection & patient monitoring using deep learning CT image analysis. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Khan, S., Rahmani, H., Shah, S.A.A., and Bennamoun, M. (2018). A Guide to Convolutional Neural Networks for Computer Vision, Morgan & Claypool. [1st ed.].","DOI":"10.1007\/978-3-031-01821-3"},{"key":"ref_38","first-page":"1","article-title":"Applications of deep convolutional neural network in computer vision","volume":"31","author":"Hongtao","year":"2016","journal-title":"J. Data Acquis. Process."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"88","DOI":"10.3389\/fnins.2020.00088","article-title":"Spatial properties of STDP in a self-learning spiking neural network enable controlling a mobile robot","volume":"14","author":"Lobov","year":"2020","journal-title":"Front. Neurosci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"97","DOI":"10.3389\/fnins.2021.601109","article-title":"Efficient spike-driven learning with dendritic event-based processing","volume":"15","author":"Yang","year":"2021","journal-title":"Front. Neurosci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1109\/TNNLS.2019.2899936","article-title":"Scalable digital neuromorphic architecture for large-scale biophysically meaningful neural network with multi-compartment neurons","volume":"31","author":"Yang","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2490","DOI":"10.1109\/TCYB.2018.2823730","article-title":"Real-time neuromorphic system for large-scale conductance-based spiking neural networks","volume":"49","author":"Yang","year":"2019","journal-title":"IEEE Trans. Cybern."},{"key":"ref_43","unstructured":"GitHub (2020, November 15). COVID-19 Chest X-ray Model. Available online: https:\/\/github.com\/aildnont\/covid-cxr."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Narin, A., Kaya, C., and Pamuk, Z. (2020). Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. arXiv.","DOI":"10.1007\/s10044-021-00984-y"},{"key":"ref_45","unstructured":"Farooq, M., and Hafeez, A. (2020). COVID-ResNet: A deep learning framework for screening of COVID19 from radiographs. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Afshar, P., Heidarian, S., Naderkhani, F., Oikonomou, A., Plataniotis, K.N., and Mohammadi, A. (2020). COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images. arXiv.","DOI":"10.3389\/frai.2021.598932"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wang, L., and Wong, A. (2020). COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. arXiv.","DOI":"10.1038\/s41598-020-76550-z"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.1007\/s10489-020-01867-1","article-title":"COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization","volume":"51","author":"Zebin","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1007\/s10489-020-01888-w","article-title":"COVIDetectioNet: COVID-19 diagnosis system based on X-ray images using features selected from pre-learned deep features ensemble","volume":"51","author":"Turkoglu","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1007\/s10489-020-01904-z","article-title":"OptCoNet: An optimized convolutional neural network for an automatic diagnosis of COVID-19","volume":"51","author":"Goel","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Misra, S., Jeon, S., Lee, S., Managuli, R., Jang, I.-S., and Kim, C. (2020). Multi-channel transfer learning of chest X-ray images for screening of COVID-19. Electronics, 9.","DOI":"10.3390\/electronics9091388"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1690","DOI":"10.1007\/s10489-020-01902-1","article-title":"Deep learning based detection and analysis of COVID-19 on chest X-ray images","volume":"51","author":"Jain","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_53","unstructured":"GitHub (2020, November 15). Actualmed COVID-19 Chest X-ray Dataset Initiative. Available online: https:\/\/github.com\/agchung\/Actualmed-COVID-chestxray-dataset."},{"key":"ref_54","unstructured":"Kaggle (2020, November 15). Covid-19 Detection from Lung X-rays. Available online: https:\/\/www.kaggle.com\/eswarchandt\/covid-19-detection-from-lung-x-rays\/."},{"key":"ref_55","unstructured":"Kaggle (2020, November 15). COVID-19 Radiography Database (Winner of the COVID-19 Dataset Award). Available online: https:\/\/www.kaggle.com\/tawsifurrahman\/covid19-radiography-database."},{"key":"ref_56","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very deep convolutional networks for large-scale image recognition. Proceedings of the International Conference on Learning Representations (ICLR), San Diego, CA, USA."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis. (IJCV)"},{"key":"ref_59","unstructured":"Tindall, L., Luong, C., and Saad, A. (2020, November 15). Plankton Classification Using VGG16 Network. Available online: https:\/\/pdfs.semanticscholar.org\/7cb1\/a0d0d30b4567b771ad7ae265ab0e935bc41c.pdf."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Ashraf, K., Wu, B., Iandola, F., Moskewicz, M., and Keutzer, K. (2016). Shallow networks for high-accuracy road object-detection. arXiv.","DOI":"10.5220\/0006214900330040"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Antony, J., McGuinness, K., O\u2019Connor, N., and Moran, K. (2016, January 4\u20138). Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks. Proceedings of the International Conference on Pattern Recognition (ICPR), Cancun, Mexico.","DOI":"10.1109\/ICPR.2016.7899799"},{"key":"ref_62","unstructured":"Keras (2020, November 15). VGG16 and VGG19. Available online: https:\/\/keras.io\/api\/applications\/vgg\/#vgg16-function."},{"key":"ref_63","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). TensorFlow: A System for large-scale machine learning. Proceedings of the USENIX Symposium on Operating Systems Design and Implementation (OSDI), Savannah, GA, USA."},{"key":"ref_64","unstructured":"PyImageSearch (2020, November 15). Available online: https:\/\/www.pyimagesearch.com."},{"key":"ref_65","unstructured":"Kingma, D., and Ba, J. (2015, January 7\u20139). Adam: A method for stochastic optimization. Proceedings of the International Conference on Learning Representations (ICLR), San Diego, CA, USA."},{"key":"ref_66","unstructured":"Keras (2020, November 15). Xception. Available online: https:\/\/keras.io\/api\/applications\/xception\/."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Alorf, A., Abbott, A.L., and Peker, K.A. (2019, January 23\u201326). Facial attribute classification: A comprehensive study and a novel mid-level fusion classifier. Proceedings of the International Conference on Biometrics Theory, Applications and Systems (BTAS), Tampa, FL, USA.","DOI":"10.1109\/BTAS46853.2019.9185985"},{"key":"ref_68","unstructured":"Abdulaziz Alorf (2021, June 10). Codes for COVID-19 Detection. Available online: https:\/\/drive.google.com\/drive\/folders\/1Wol6DQABuEOTBzTK-spBEu6z_QO6e-mE?usp=sharing."},{"key":"ref_69","unstructured":"Simonyan, K., Vedaldi, A., and Zisserman, A. (2013). Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Selvaraju, R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-CAM: Visual explanations from deep networks via gradient-based localization. Proceedings of the International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_71","unstructured":"Lundberg, S., and Lee, S. (2017, January 4\u20139). A unified approach to interpreting model predictions. Proceedings of the International Conference on Neural Information Processing Systems (NIPS), Long Beach, CA, USA."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1038\/s41551-018-0304-0","article-title":"Explainable machine-learning predictions for the prevention of hypoxaemia during surgery","volume":"2","author":"Lundberg","year":"2018","journal-title":"Nat. Biomed. Eng."},{"key":"ref_74","unstructured":"GitHub (2020, November 15). SHAP. Available online: https:\/\/github.com\/slundberg\/shap."},{"key":"ref_75","unstructured":"Kaggle (2020, November 15). Machine Learning Explainability Home Page. Available online: https:\/\/www.kaggle.com\/dansbecker\/shap-values."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/6\/183\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:13:50Z","timestamp":1760163230000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/6\/183"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,13]]},"references-count":75,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["a14060183"],"URL":"https:\/\/doi.org\/10.3390\/a14060183","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,13]]}}}