{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T23:15:48Z","timestamp":1776381348567,"version":"3.51.2"},"reference-count":65,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,4,20]],"date-time":"2020-04-20T00:00:00Z","timestamp":1587340800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The coronavirus (COVID-19) pandemic is putting healthcare systems across the world under unprecedented and increasing pressure according to the World Health Organization (WHO). With the advances in computer algorithms and especially Artificial Intelligence, the detection of this type of virus in the early stages will help in fast recovery and help in releasing the pressure off healthcare systems. In this paper, a GAN with deep transfer learning for coronavirus detection in chest X-ray images is presented. The lack of datasets for COVID-19 especially in chest X-rays images is the main motivation of this scientific study. The main idea is to collect all the possible images for COVID-19 that exists until the writing of this research and use the GAN network to generate more images to help in the detection of this virus from the available X-rays images with the highest accuracy possible. The dataset used in this research was collected from different sources and it is available for researchers to download and use it. The number of images in the collected dataset is 307 images for four different types of classes. The classes are the COVID-19, normal, pneumonia bacterial, and pneumonia virus. Three deep transfer models are selected in this research for investigation. The models are the Alexnet, Googlenet, and Restnet18. Those models are selected for investigation through this research as it contains a small number of layers on their architectures, this will result in reducing the complexity, the consumed memory and the execution time for the proposed model. Three case scenarios are tested through the paper, the first scenario includes four classes from the dataset, while the second scenario includes 3 classes and the third scenario includes two classes. All the scenarios include the COVID-19 class as it is the main target of this research to be detected. In the first scenario, the Googlenet is selected to be the main deep transfer model as it achieves 80.6% in testing accuracy. In the second scenario, the Alexnet is selected to be the main deep transfer model as it achieves 85.2% in testing accuracy, while in the third scenario which includes two classes (COVID-19, and normal), Googlenet is selected to be the main deep transfer model as it achieves 100% in testing accuracy and 99.9% in the validation accuracy. All the performance measurement strengthens the obtained results through the research.<\/jats:p>","DOI":"10.3390\/sym12040651","type":"journal-article","created":{"date-parts":[[2020,4,21]],"date-time":"2020-04-21T05:48:52Z","timestamp":1587448132000},"page":"651","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":449,"title":["Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3849-4566","authenticated-orcid":false,"given":"Mohamed","family":"Loey","sequence":"first","affiliation":[{"name":"Department of Computer Science, Faculty of Computers and Artificial Intelligence, Benha University, Benha 13511, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5560-5926","authenticated-orcid":false,"given":"Florentin","family":"Smarandache","sequence":"additional","affiliation":[{"name":"Department of Mathematics, University of New Mexico, Gallup Campus, NM 87301, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8614-9057","authenticated-orcid":false,"given":"Nour Eldeen","family":"M. Khalifa","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Faculty of Computers and Artificial Intelligence, Cairo University, Cairo 12613, Egypt"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/s12098-020-03263-6","article-title":"A Review of Coronavirus Disease-2019 (COVID-19)","volume":"87","author":"Singhal","year":"2020","journal-title":"Indian J. Pediatrics"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"105924","DOI":"10.1016\/j.ijantimicag.2020.105924","article-title":"Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges","volume":"55","author":"Lai","year":"2020","journal-title":"Int. J. Antimicrob. Agents"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/S1473-3099(20)30063-3","article-title":"Game consumption and the 2019 novel coronavirus","volume":"20","author":"Li","year":"2020","journal-title":"Lancet Infect. Dis."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sharfstein, J.M., Becker, S.J., and Mello, M.M. (2020). Diagnostic Testing for the Novel Coronavirus. JAMA.","DOI":"10.1001\/jama.2020.3864"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chang, L., Yan, Y., and Wang, L. (2020). Coronavirus Disease 2019: Coronaviruses and Blood Safety. Transfus. Med. Rev.","DOI":"10.1016\/j.tmrv.2020.02.003"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.jare.2020.03.005","article-title":"COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses","volume":"24","author":"Shereen","year":"2020","journal-title":"J. Adv. Res."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Rabi, F.A., Al Zoubi, M.S., Kasasbeh, G.A., Salameh, D.M., and Al-Nasser, A.D. (2020). SARS-CoV-2 and Coronavirus Disease 2019: What We Know So Far. Pathogens, 9.","DOI":"10.3390\/pathogens9030231"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1038\/s41579-020-0336-9","article-title":"Novel coronavirus takes flight from bats?","volume":"18","author":"York","year":"2020","journal-title":"Nat. Rev. Microbiol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lam, T.T.-Y., Shum, M.H.-H., Zhu, H.-C., Tong, Y.-G., Ni, X.-B., Liao, Y.-S., Wei, W., Cheung, W.Y.-M., Li, W.-J., and Li, L.-F. (2020). Identifying SARS-CoV-2 related coronaviruses in Malayan pangolins. Nature, 1\u20136.","DOI":"10.1038\/s41586-020-2169-0"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1002\/jmv.25699","article-title":"The first two cases of 2019-nCoV in Italy: Where they come from?","volume":"92","author":"Giovanetti","year":"2020","journal-title":"J. Med. Virol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1056\/NEJMoa2001191","article-title":"First Case of 2019 Novel Coronavirus in the United States","volume":"382","author":"Holshue","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/S1473-3099(20)30067-0","article-title":"The first 2019 novel coronavirus case in Nepal","volume":"20","author":"Bastola","year":"2020","journal-title":"Lancet Infect. Dis."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"970","DOI":"10.1056\/NEJMc2001468","article-title":"Transmission of 2019-nCoV Infection from an Asymptomatic Contact in Germany","volume":"382","author":"Rothe","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1056\/NEJMc2001272","article-title":"Importation and Human-to-Human Transmission of a Novel Coronavirus in Vietnam","volume":"382","author":"Phan","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"ref_15","unstructured":"(2020, March 31). Coronavirus (COVID-19) Map. Available online: https:\/\/www.google.com\/COVID-19-map\/."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1016\/j.compag.2019.05.019","article-title":"Computer vision detection of foreign objects in walnuts using deep learning","volume":"162","author":"Rong","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1038\/s41576-019-0122-6","article-title":"Deep learning: new computational modelling techniques for genomics","volume":"20","author":"Eraslan","year":"2019","journal-title":"Nat. Rev. Genet."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.tibtech.2018.08.005","article-title":"Deep Learning with Microfluidics for Biotechnology","volume":"37","author":"Riordon","year":"2019","journal-title":"Trends Biotechnol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.zemedi.2018.11.002","article-title":"An overview of deep learning in medical imaging focusing on MRI","volume":"29","author":"Lundervold","year":"2019","journal-title":"Z. f\u00fcr Med. Phys."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.zemedi.2018.12.003","article-title":"A gentle introduction to deep learning in medical image processing","volume":"29","author":"Maier","year":"2019","journal-title":"Z. f\u00fcr Med. Phys."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"53040","DOI":"10.1109\/ACCESS.2019.2912200","article-title":"Review of Deep Learning Algorithms and Architectures","volume":"7","author":"Shrestha","year":"2019","journal-title":"IEEE Access"},{"key":"ref_22","first-page":"1","article-title":"A Survey on Deep Learning: Algorithms, Techniques, and Applications","volume":"51","author":"Pouyanfar","year":"2018","journal-title":"ACM Comput. Surv."},{"key":"ref_23","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative Adversarial Nets. Proceedings of the 27th International Conference on Neural Information Processing Systems\u2014Volume 2, Montreal, QC, Canada."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"14985","DOI":"10.1109\/ACCESS.2018.2886814","article-title":"Recent Advances of Generative Adversarial Networks in Computer Vision","volume":"7","author":"Cao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Gonog, L., and Zhou, Y. (2019, January 19\u201321). A Review: Generative Adversarial Networks. Proceedings of the 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA), Xi\u2019an, China.","DOI":"10.1109\/ICIEA.2019.8833686"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"28158","DOI":"10.1109\/ACCESS.2019.2899108","article-title":"Controllable Generative Adversarial Network","volume":"7","author":"Lee","year":"2019","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Caramihale, T., Popescu, D., and Ichim, L. (2018). Emotion Classification Using a Tensorflow Generative Adversarial Network Implementation. Symmetry, 10.","DOI":"10.3390\/sym10090414"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ciregan, D., Meier, U., and Schmidhuber, J. (2012, January 16\u201321). Multi-column deep neural networks for image classification. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248110"},{"key":"ref_29","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst., 1097\u20131105."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yin, F., Wang, Q., Zhang, X., and Liu, C. (2013, January 25\u201328). ICDAR 2013 Chinese Handwriting Recognition Competition. Proceedings of the 2013 12th International Conference on Document Analysis and Recognition, Washington, DC, USA.","DOI":"10.1109\/ICDAR.2013.218"},{"key":"ref_32","unstructured":"Hassanien, A.E., Shaalan, K., Gaber, T., Azar, A.T., and Tolba, M.F. (2016, January 24\u201326). CNN for Handwritten Arabic Digits Recognition Based on LeNet-5 BT. Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016, Cairo, Egypt."},{"key":"ref_33","first-page":"11","article-title":"Arabic Handwritten Characters Recognition Using Convolutional Neural Network","volume":"5","author":"Loey","year":"2017","journal-title":"WSEAS Trans. Comput. Res."},{"key":"ref_34","unstructured":"LeCun, Y., Huang, F.J., and Bottou, L. (July, January 27). Learning methods for generic object recognition with invariance to pose and lighting. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, Washington, DC, USA."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Stallkamp, J., Schlipsing, M., Salmen, J., and Igel, C. (August, January 31). The German Traffic Sign Recognition Benchmark: A multi-class classification competition. Proceedings of the The 2011 International Joint Conference on Neural Networks, San Jose, CA, USA.","DOI":"10.1109\/IJCNN.2011.6033395"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L., Kai, L., and Li, F.-F. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Liu, S., and Deng, W. (2015, January 3\u20136). Very deep convolutional neural network based image classification using small training sample size. Proceedings of the 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ACPR.2015.7486599"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Wei, L., Yangqing, J., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 24\u201326). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Cairo, Egypt.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_41","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (1\u20131, January 26). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4180949","DOI":"10.1155\/2019\/4180949","article-title":"An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare","volume":"2019","author":"Stephen","year":"2019","journal-title":"J. Healthc. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning","volume":"172","author":"Kermany","year":"2018","journal-title":"Cell"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Ayan, E., and \u00dcnver, H.M. (2019, January 24\u201326). Diagnosis of Pneumonia from Chest X-ray Images Using Deep Learning. Proceedings of the 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), Istanbul, Turkey.","DOI":"10.1109\/EBBT.2019.8741582"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Varshni, D., Thakral, K., Agarwal, L., Nijhawan, R., and Mittal, A. (2019, January 20\u201322). Pneumonia Detection Using CNN based Feature Extraction. Proceedings of the 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, India.","DOI":"10.1109\/ICECCT.2019.8869364"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., and Summers, R.M. (2017, January 21\u201326). ChestX-ray8: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.369"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chouhan, V., Singh, S.K., Khamparia, A., Gupta, D., Tiwari, P., Moreira, C., Dama\u0161evi\u010dius, R., and de Albuquerque, V.H.C. (2020). A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images. Appl. Sci., 10.","DOI":"10.3390\/app10020559"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Islam, S.R., Maity, S.P., Ray, A.K., and Mandal, M. (2019, January 5\u20138). Automatic Detection of Pneumonia on Compressed Sensing Images using Deep Learning. Proceedings of the 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), Edmonton, AB, Canada.","DOI":"10.1109\/CCECE.2019.8861969"},{"key":"ref_50","unstructured":"Cohen, J.P., Morrison, P., and Dao, L. (2020). COVID-19 Image Data Collection. arXiv."},{"key":"ref_51","unstructured":"Cohen, J.P., Morrison, P., and Dao, L. (2020, March 31). COVID-19 Image Data Collection. Available online: https:\/\/github.com\/ieee8023\/covid-chestxray-dataset."},{"key":"ref_52","unstructured":"(2020, March 31). Dataset. Available online: https:\/\/drive.google.com\/uc?id=1coM7x3378f-Ou2l6Pg2wldaOI7Dntu1a."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"96","DOI":"10.5455\/aim.2019.27.96-102","article-title":"Deep Iris: Deep Learning for Gender Classification Through Iris Patterns","volume":"27","author":"Khalifa","year":"2019","journal-title":"Acta Inform. Medica"},{"key":"ref_54","first-page":"256","article-title":"Deep bacteria: robust deep learning data augmentation design for limited bacterial colony dataset","volume":"11","author":"Khalifa","year":"2019","journal-title":"Int. J. Reason.-Based Intell. Syst."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"5858","DOI":"10.1109\/ACCESS.2017.2696121","article-title":"Smart Augmentation Learning an Optimal Data Augmentation Strategy","volume":"5","author":"Lemley","year":"2017","journal-title":"IEEE Access"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"22874","DOI":"10.1109\/ACCESS.2020.2970210","article-title":"Artificial Intelligence Technique for Gene Expression by Tumor RNA-Seq Data: A Novel Optimized Deep Learning Approach","volume":"8","author":"Khalifa","year":"2020","journal-title":"IEEE Access"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"327","DOI":"10.5455\/aim.2019.27.327-332","article-title":"Deep Transfer Learning Models for Medical Diabetic Retinopathy Detection","volume":"27","author":"Khalifa","year":"2019","journal-title":"Acta Inform. Medica"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Khalifa, N.E., Hamed Taha, M., Hassanien, A.E., and Selim, I. (2018, January 11\u201313). Deep galaxy V2: Robust deep convolutional neural networks for galaxy morphology classifications. Proceedings of the 2018 International Conference on Computing Sciences and Engineering, ICCSE 2018\u2014Proceedings, Kuwait City, Kuwait.","DOI":"10.1109\/ICCSE1.2018.8374210"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Khalifa, N.E.M., Taha, M.H.N., and Hassanien, A.E. (2018). Aquarium Family Fish Species Identification System Using Deep Neural Networks. International Conference on Advanced Intelligent Systems and Informatics, Springer.","DOI":"10.1007\/978-3-319-99010-1_32"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Aswathy, P., and Mishra, D. (2018, January 1\u20132). Deep GoogLeNet Features for Visual Object Tracking. Proceedings of the 2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS), Rupnagar, India.","DOI":"10.1109\/ICIINFS.2018.8721317"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A.A. (2017, January 4\u20139). Inception-v4, inception-ResNet and the impact of residual connections on learning. Proceedings of the 31st AAAI Conference on Artificial Intelligence, AAAI 2017, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Montavon, G., Orr, G.B., and M\u00fcller, K.-R. (2012). Stochastic Gradient Descent Tricks. Neural Networks: Tricks of the Trade: Second Edition, Springer.","DOI":"10.1007\/978-3-642-35289-8"},{"key":"ref_63","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_64","unstructured":"Caruana, R., Lawrence, S., and Giles, L. Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping. Proceedings of the 13th International Conference on Neural Information Processing Systems."},{"key":"ref_65","unstructured":"Goutte, C., and Gaussier, E. (2010). A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation. European Conference on Information Retrieval, Springer."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/4\/651\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:09:06Z","timestamp":1760364546000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/4\/651"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,20]]},"references-count":65,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["sym12040651"],"URL":"https:\/\/doi.org\/10.3390\/sym12040651","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,20]]}}}