{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T17:01:01Z","timestamp":1781370061836,"version":"3.54.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-023-00941-7","type":"journal-article","created":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T18:02:28Z","timestamp":1705082548000},"page":"778-800","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Enhancing Disease Classification with Deep Learning: a Two-Stage Optimization Approach for Monkeypox and Similar Skin Lesion Diseases"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3440-6271","authenticated-orcid":false,"given":"Serkan","family":"Sava\u015f","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,12]]},"reference":[{"key":"941_CR1","unstructured":"WHO. (2023). Mpox (monkeypox). Mpox (Monkeypox). https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/monkeypox"},{"key":"941_CR2","doi-asserted-by":"crossref","unstructured":"Haque, Md. E., Ahmed, Md. R., Nila, R. S., & Islam, S. (2022a). Classification of Human Monkeypox Disease Using Deep Learning Models and Attention Mechanisms. https:\/\/arxiv.org\/abs\/2211.15459v1","DOI":"10.1109\/ICCIT57492.2022.10055870"},{"key":"941_CR3","unstructured":"CDC. (2022). About Chickenpox. About Chickenpox. https:\/\/www.cdc.gov\/chickenpox\/about\/index.html#"},{"key":"941_CR4","unstructured":"NHS. (2022). Measles. Measles. https:\/\/www.nhs.uk\/conditions\/measles\/"},{"key":"941_CR5","doi-asserted-by":"publisher","unstructured":"Delidow, B. C., Lynch, J. P., Peluso, J. J., & White, B. A. (1993). Polymerase Chain Reaction. In B. A. White (Ed.), PCR Protocols: Current Methods and Applications (pp. 1\u201329). Humana Press. https:\/\/doi.org\/10.1385\/0-89603-244-2:1","DOI":"10.1385\/0-89603-244-2:1"},{"issue":"1","key":"941_CR6","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1093\/infdis\/jiac317","volume":"227","author":"RN Binny","year":"2023","unstructured":"Binny, R. N., Priest, P., French, N. P., Parry, M., Lustig, A., Hendy, S. C., Maclaren, O. J., Ridings, K. M., Steyn, N., Vattiato, G., & Plank, M. J. (2023). Sensitivity of Reverse Transcription Polymerase Chain Reaction Tests for Severe Acute Respiratory Syndrome Coronavirus 2 Through Time. The Journal of Infectious Diseases, 227(1), 9\u201317. https:\/\/doi.org\/10.1093\/infdis\/jiac317","journal-title":"The Journal of Infectious Diseases"},{"issue":"1","key":"941_CR7","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1186\/s12985-021-01489-0","volume":"18","author":"JN Kanji","year":"2021","unstructured":"Kanji, J. N., Zelyas, N., MacDonald, C., Pabbaraju, K., Khan, M. N., Prasad, A., Hu, J., Diggle, M., Berenger, B. M., & Tipples, G. (2021). False negative rate of COVID-19 PCR testing: a discordant testing analysis. Virology Journal, 18(1), 13. https:\/\/doi.org\/10.1186\/s12985-021-01489-0","journal-title":"Virology Journal"},{"issue":"4","key":"941_CR8","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1108\/SASBE-07-2019-0083","volume":"9","author":"A Aggarwal","year":"2020","unstructured":"Aggarwal, A., Rani, A., & Kumar, M. (2020). A robust method to authenticate car license plates using segmentation and ROI based approach. Smart and Sustainable Built Environment, 9(4), 737\u2013747. https:\/\/doi.org\/10.1108\/SASBE-07-2019-0083","journal-title":"Smart and Sustainable Built Environment"},{"key":"941_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/J.JVOICE.2023.06.014","author":"G Aggarwal","year":"2023","unstructured":"Aggarwal, G., Jhajharia, K., Izhar, J., Kumar, M., & Abualigah, L. (2023). A Machine Learning Approach to Classify Biomedical Acoustic Features for Baby Cries. Journal of Voice. https:\/\/doi.org\/10.1016\/J.JVOICE.2023.06.014","journal-title":"Journal of Voice"},{"key":"941_CR10","doi-asserted-by":"publisher","unstructured":"Alhudhaif, A., Almaslukh, B., Aseeri, A. O., Guler, O., & Polat, K. (2023). A novel nonlinear automated multi-class skin lesion detection system using soft-attention based convolutional neural networks. Chaos, Solitons & Fractals, 170, 113409. https:\/\/doi.org\/10.1016\/j.chaos.2023.113409","DOI":"10.1016\/j.chaos.2023.113409"},{"key":"941_CR11","doi-asserted-by":"publisher","unstructured":"G\u00fcler, O., & Polat, K. (2022). Classification Performance of Deep Transfer Learning Methods for Pneumonia Detection from Chest X-Ray Images. Journal of Artificial Intelligence and Systems, 4(1), 107\u2013126. https:\/\/doi.org\/10.33969\/AIS.2022040107","DOI":"10.33969\/AIS.2022040107"},{"key":"941_CR12","unstructured":"B\u00fct\u00fcner, R., & Calp, M. H. (2022). Diagnosis and Detection of COVID-19 from Lung Tomography Images Using Deep Learning and Machine Learning Methods. International Journal of Intelligent Systems and Applications in Engineering, 10(2), 190\u2013200. https:\/\/ijisae.org\/index.php\/IJISAE\/article\/view\/1843"},{"issue":"19","key":"941_CR13","doi-asserted-by":"publisher","first-page":"13755","DOI":"10.1007\/s00521-021-06376-x","volume":"35","author":"S Raheja","year":"2023","unstructured":"Raheja, S., Kasturia, S., Cheng, X., & Kumar, M. (2023). Machine learning-based diffusion model for prediction of coronavirus-19 outbreak. Neural Computing and Applications, 35(19), 13755\u201313774. https:\/\/doi.org\/10.1007\/s00521-021-06376-x","journal-title":"Neural Computing and Applications"},{"key":"941_CR14","doi-asserted-by":"publisher","unstructured":"Al-Saedi, D. K. A., & Sava\u015f, S. (2022). Classification of Skin Cancer with Deep Transfer Learning Method. Computer Science, IDAP-2022(International Artificial Intelligence and Data Processing Symposium), 202\u2013210. https:\/\/doi.org\/10.53070\/BBD.1172782","DOI":"10.53070\/BBD.1172782"},{"issue":"23","key":"941_CR15","doi-asserted-by":"publisher","first-page":"34105","DOI":"10.1007\/s11042-022-13008-6","volume":"81","author":"G Madhu","year":"2022","unstructured":"Madhu, G., Govardhan, A., Ravi, V., Kautish, S., Srinivas, B. S., Chaudhary, T., & Kumar, M. (2022). DSCN-net: a deep Siamese capsule neural network model for automatic diagnosis of malaria parasites detection. Multimedia Tools and Applications, 81(23), 34105\u201334127. https:\/\/doi.org\/10.1007\/s11042-022-13008-6","journal-title":"Multimedia Tools and Applications"},{"key":"941_CR16","doi-asserted-by":"publisher","unstructured":"Alhatemi, R. A. J., & Sava\u015f, S. (2022). Transfer Learning-Based Classification Comparison of Stroke. Computer Science, IDAP 2022:(International Artificial Intelligence and Data Processing Symposium), 192\u2013201. https:\/\/doi.org\/10.53070\/BBD.1172807","DOI":"10.53070\/BBD.1172807"},{"issue":"1","key":"941_CR17","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1111\/JGH.15327","volume":"36","author":"H Chen","year":"2021","unstructured":"Chen, H., & Sung, J. J. Y. (2021). Potentials of AI in medical image analysis in Gastroenterology and Hepatology. Journal of Gastroenterology and Hepatology, 36(1), 31\u201338. https:\/\/doi.org\/10.1111\/JGH.15327","journal-title":"Journal of Gastroenterology and Hepatology"},{"key":"941_CR18","doi-asserted-by":"publisher","unstructured":"Kolla, L., Gruber, F. K., Khalid, O., Hill, C., & Parikh, R. B. (2021). The case for AI-driven cancer clinical trials \u2013 The efficacy arm in silico. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer, 1876(1), 188572. https:\/\/doi.org\/10.1016\/J.BBCAN.2021.188572","DOI":"10.1016\/J.BBCAN.2021.188572"},{"key":"941_CR19","unstructured":"Ahsan, M. M., Uddin, M. R., Farjana, M., Sakib, A. N., Momin, K. Al, & Luna, S. A. (2022). Image Data collection and implementation of deep learning-based model in detecting Monkeypox disease using modified VGG16. https:\/\/arxiv.org\/abs\/2206.01862v1"},{"key":"941_CR20","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1016\/J.NEUNET.2023.02.022","volume":"161","author":"D Bala","year":"2023","unstructured":"Bala, D., Hossain, M. S., Hossain, M. A., Abdullah, M. I., Rahman, M. M., Manavalan, B., Gu, N., Islam, M. S., & Huang, Z. (2023). MonkeyNet: A robust deep convolutional neural network for monkeypox disease detection and classification. Neural Networks, 161, 757\u2013775. https:\/\/doi.org\/10.1016\/J.NEUNET.2023.02.022","journal-title":"Neural Networks"},{"key":"941_CR21","first-page":"106","volume":"40","author":"KD Ak\u0131n","year":"2022","unstructured":"Ak\u0131n, K. D., G\u00fcrkan, \u00c7., Budak, A., & Karatas, H. (2022). Classification of Monkeypox Skin Lesion using the Explainable Artificial Intelligence Assisted Convolutional Neural Networks. European Journal of Science and Technology, 40, 106\u2013110.","journal-title":"European Journal of Science and Technology"},{"key":"941_CR22","unstructured":"Ya\u015far, H. (2022). Transfer Derin \u00d6\u011frenme Kullan\u0131larak Maymun \u00c7i\u00e7e\u011fi Hastal\u0131\u011f\u0131n\u0131n \u0130ki S\u0131n\u0131fl\u0131 ve \u00c7ok S\u0131n\u0131fl\u0131 S\u0131n\u0131fland\u0131r\u0131lmas\u0131 \u00dczerine Kapsaml\u0131 Bir \u00c7al\u0131\u015fma. ELECO 2022 - Elektrik-Elektronik ve Biyomedikal M\u00fchendisli\u011fi Konferans\u0131, 1\u20135."},{"key":"941_CR23","doi-asserted-by":"crossref","unstructured":"Haque, Md. E., Ahmed, Md. R., Nila, R. S., & Islam, S. (2022b). Classification of Human Monkeypox Disease Using Deep Learning Models and Attention Mechanisms. ArXiv. https:\/\/arxiv.org\/abs\/2211.15459v1","DOI":"10.1109\/ICCIT57492.2022.10055870"},{"key":"941_CR24","doi-asserted-by":"publisher","unstructured":"Dwivedi, M., Tiwari, R. G., & Ujjwal, N. (2022). Deep Learning Methods for Early Detection of Monkeypox Skin Lesion. 2022 8th International Conference on Signal Processing and Communication, ICSC 2022, 343\u2013348. https:\/\/doi.org\/10.1109\/ICSC56524.2022.10009571","DOI":"10.1109\/ICSC56524.2022.10009571"},{"key":"941_CR25","doi-asserted-by":"publisher","unstructured":"Uzun Ozsahin, D., Mustapha, M. T., Uzun, B., Duwa, B., & Ozsahin, I. (2023). Computer-Aided Detection and Classification of Monkeypox and Chickenpox Lesion in Human Subjects Using Deep Learning Framework. Diagnostics, 13(2). https:\/\/doi.org\/10.3390\/diagnostics13020292","DOI":"10.3390\/diagnostics13020292"},{"issue":"4","key":"941_CR26","doi-asserted-by":"publisher","DOI":"10.1371\/JOURNAL.PONE.0281815","volume":"18","author":"R Pramanik","year":"2023","unstructured":"Pramanik, R., Banerjee, B., Efimenko, G., Kaplun, D., & Sarkar, R. (2023). Monkeypox detection from skin lesion images using an amalgamation of CNN models aided with Beta function-based normalization scheme. PLOS ONE, 18(4), e0281815. https:\/\/doi.org\/10.1371\/JOURNAL.PONE.0281815","journal-title":"PLOS ONE"},{"key":"941_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIPTEKNO56568.2022.9960194","volume":"2022","author":"MC Irmak","year":"2022","unstructured":"Irmak, M. C., Aydin, T., & Ya\u011fano\u011flu, M. (2022). Monkeypox Skin Lesion Detection with MobileNetV2 and VGGNet Models. 2022 Medical Technologies Congress (TIPTEKNO), 1\u20134. https:\/\/doi.org\/10.1109\/TIPTEKNO56568.2022.9960194","journal-title":"Medical Technologies Congress (TIPTEKNO)"},{"issue":"11","key":"941_CR28","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1007\/s10916-022-01863-7","volume":"46","author":"VH Sahin","year":"2022","unstructured":"Sahin, V. H., Oztel, I., & Yolcu Oztel, G. (2022). Human Monkeypox Classification from Skin Lesion Images with Deep Pre-trained Network using Mobile Application. Journal of Medical Systems, 46(11), 79. https:\/\/doi.org\/10.1007\/s10916-022-01863-7","journal-title":"Journal of Medical Systems"},{"issue":"11","key":"941_CR29","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1007\/s10916-022-01868-2","volume":"46","author":"C Sitaula","year":"2022","unstructured":"Sitaula, C., & Shahi, T. B. (2022). Monkeypox Virus Detection Using Pre-trained Deep Learning-based Approaches. Journal of Medical Systems, 46(11), 78. https:\/\/doi.org\/10.1007\/s10916-022-01868-2","journal-title":"Journal of Medical Systems"},{"key":"941_CR30","doi-asserted-by":"publisher","unstructured":"Altun, M., G\u00fcr\u00fcler, H., \u00d6zkaraca, O., Khan, F., Khan, J., & Lee, Y. (2023). Monkeypox Detection Using CNN with Transfer Learning. Sensors, 23(4). https:\/\/doi.org\/10.3390\/s23041783","DOI":"10.3390\/s23041783"},{"key":"941_CR31","doi-asserted-by":"publisher","unstructured":"Bala, D., & Hossain, M. S. (2023). Monkeypox Skin Images Dataset (MSID). 6. https:\/\/doi.org\/10.17632\/R9BFPNVYXR.6","DOI":"10.17632\/R9BFPNVYXR.6"},{"key":"941_CR32","doi-asserted-by":"publisher","unstructured":"Cubuk, E. D., Zoph, B., Shlens, J., & Le, Q. V. (2019). RandAugment: Practical automated data augmentation with a reduced search space. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2020-June, 3008\u20133017. https:\/\/doi.org\/10.1109\/CVPRW50498.2020.00359","DOI":"10.1109\/CVPRW50498.2020.00359"},{"issue":"1","key":"941_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/S40537-019-0197-0\/FIGURES\/33","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on Image Data Augmentation for Deep Learning. Journal of Big Data, 6(1), 1\u201348. https:\/\/doi.org\/10.1186\/S40537-019-0197-0\/FIGURES\/33","journal-title":"Journal of Big Data"},{"issue":"10","key":"941_CR34","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345\u20131359. https:\/\/doi.org\/10.1109\/TKDE.2009.191","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"1","key":"941_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/S40537-016-0043-6\/TABLES\/6","volume":"3","author":"K Weiss","year":"2016","unstructured":"Weiss, K., Khoshgoftaar, T. M., & Wang, D. D. (2016). A survey of transfer learning. Journal of Big Data, 3(1), 1\u201340. https:\/\/doi.org\/10.1186\/S40537-016-0043-6\/TABLES\/6","journal-title":"Journal of Big Data"},{"key":"941_CR36","unstructured":"Keras. (2023). Keras Applications. Keras Applications. https:\/\/keras.io\/api\/applications\/"},{"key":"941_CR37","unstructured":"TensorFlow. (2023). Module: tf.keras.applications | TensorFlow v2.12.0. Module: Tf.Keras.Applications | TensorFlow v2.12.0. https:\/\/www.tensorflow.org\/api_docs\/python\/tf\/keras\/applications"},{"key":"941_CR38","doi-asserted-by":"publisher","unstructured":"Brown, G. (2010). Ensemble Learning. In G. I. Sammut Claude and Webb (Ed.), Encyclopedia of Machine Learning (pp. 312\u2013320). Springer US. https:\/\/doi.org\/10.1007\/978-0-387-30164-8_252","DOI":"10.1007\/978-0-387-30164-8_252"},{"issue":"3\u20134","key":"941_CR39","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1561\/2000000039","volume":"7","author":"L Deng","year":"2013","unstructured":"Deng, L., & Yu, D. (2013). Deep learning: Methods and applications. Foundations and Trends in Signal Processing, 7(3\u20134), 197\u2013387. https:\/\/doi.org\/10.1561\/2000000039","journal-title":"Foundations and Trends in Signal Processing"},{"issue":"3","key":"941_CR40","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/MCAS.2006.1688199","volume":"6","author":"R Polikar","year":"2006","unstructured":"Polikar, R. (2006). Ensemble based systems in decision making. IEEE Circuits and Systems Magazine, 6(3), 21\u201344. https:\/\/doi.org\/10.1109\/MCAS.2006.1688199","journal-title":"IEEE Circuits and Systems Magazine"},{"key":"941_CR41","doi-asserted-by":"publisher","DOI":"10.1002\/9781119995784","volume-title":"Dirichlet and Related Distributions: Theory, Methods and Applications","author":"KW Ng","year":"2011","unstructured":"Ng, K. W., Tian, G. L., & Tang, M. L. (2011). Dirichlet and Related Distributions: Theory, Methods and Applications. In Dirichlet and Related Distributions: Theory, Methods and Applications. John Wiley & Sons, Ltd. https:\/\/doi.org\/10.1002\/9781119995784"},{"key":"941_CR42","unstructured":"Borges, J. (2019). DeepStack: Ensembles for Deep Learning. https:\/\/github.com\/jcborges\/DeepStack"},{"key":"941_CR43","doi-asserted-by":"publisher","unstructured":"Howard, J., & Ruder, S. (2018). Universal Language Model Fine-tuning for Text Classification. ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers), 1, 328\u2013339. https:\/\/doi.org\/10.18653\/v1\/p18-1031","DOI":"10.18653\/v1\/p18-1031"},{"key":"941_CR44","unstructured":"Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems, 4(January), 3320\u20133328. https:\/\/arxiv.org\/abs\/1411.1792v1"},{"key":"941_CR45","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research, 15(56), 1929\u20131958. http:\/\/jmlr.org\/papers\/v15\/srivastava14a.html"},{"key":"941_CR46","unstructured":"Gao, B., & Pavel, L. (2017). On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning. https:\/\/arxiv.org\/abs\/1704.00805v4"},{"key":"941_CR47","unstructured":"Bock, S., Goppold, J., & Wei\u00df, M. (2018). An improvement of the convergence proof of the ADAM-Optimizer. https:\/\/arxiv.org\/abs\/1804.10587v1"},{"key":"941_CR48","unstructured":"Kingma, D. P., & Ba, J. L. (2014). Adam: A Method for Stochastic Optimization. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings. https:\/\/arxiv.org\/abs\/1412.6980v9"},{"key":"941_CR49","unstructured":"Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press."},{"key":"941_CR50","unstructured":"G\u00f3mez, R. (2018). Understanding Categorical Cross-Entropy Loss, Binary Cross-Entropy Loss, Softmax Loss, Logistic Loss, Focal Loss and all those confusing names. Github. https:\/\/gombru.github.io\/2018\/05\/23\/cross_entropy_loss\/"},{"key":"941_CR51","unstructured":"WHO. (2022). Second meeting of the International Health Regulations (2005) (IHR) Emergency Committee regarding the multi-country outbreak of monkeypox. Second Meeting of the International Health Regulations (2005) (IHR) Emergency Committee Regarding the Multi-Country Outbreak of Monkeypox. https:\/\/www.who.int\/news\/item\/23-07-2022-second-meeting-of-the-international-health-regulations-(2005)-(ihr)-emergency-committee-regarding-the-multi-country-outbreak-of-monkeypox"},{"issue":"7","key":"941_CR52","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1001\/JAMA.2022.12513","volume":"328","author":"JB Nuzzo","year":"2022","unstructured":"Nuzzo, J. B., Borio, L. L., & Gostin, L. O. (2022). The WHO Declaration of Monkeypox as a Global Public Health Emergency. JAMA, 328(7), 615\u2013617. https:\/\/doi.org\/10.1001\/JAMA.2022.12513","journal-title":"JAMA"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-023-00941-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-023-00941-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-023-00941-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,19]],"date-time":"2024-04-19T15:06:39Z","timestamp":1713539199000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-023-00941-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,12]]},"references-count":52,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["941"],"URL":"https:\/\/doi.org\/10.1007\/s10278-023-00941-7","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,12]]},"assertion":[{"value":"27 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 September 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 October 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Only data coming from publicly available datasets were used and ethics approval not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The author declares no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}