{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T14:50:50Z","timestamp":1782226250674,"version":"3.54.5"},"reference-count":18,"publisher":"Galenos Yayinevi","issue":"3","license":[{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Turk Gogus Kalp Dama"],"accepted":{"date-parts":[[2024,4,15]]},"published-print":{"date-parts":[[2024,7,1]]},"abstract":"<jats:p>\n                    <jats:bold>Background:<\/jats:bold>\n                    Due to the high mortality of lung cancer, the aim was to find convolutional neural network models that can distinguish benign and malignant cases with high accuracy, which can help in early diagnosis with diagnostic imaging.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Methods:<\/jats:bold>\n                    Patients who underwent tomography in our clinic and who were found to have lung nodules were retrospectively screened between January 2015 and December 2020. The patients were divided into two groups: benign (n=68; 38 males, 30 females; mean age: 59\u00b112.2 years; range, 27 to 81 years) and malignant (n=29; 19 males, 10 females; mean age: 65\u00b110.4 years; range, 43 to 88 years). In addition, a control group (n=67; 38 males, 29 females; mean age: 56.9\u00b114.1 years; range, 26 to 81 years) consisting of healthy patients with no pathology in their sections was formed. Deep neural networks were trained with 80% of the three-class dataset we created and tested with 20% of the data. After the training of deep neural networks, feature extraction was done for these networks. The features extracted from the dataset were classified by machine learning algorithms. Performance results were obtained using confusion matrix analysis.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Results:<\/jats:bold>\n                    After training deep neural networks, the highest accuracy rate of 80% was achieved with the AlexNET model among the models used. In the second stage results, obtained after feature extraction and using the classifier, the highest accuracy rate was achieved with the support vector machine classifier in the VGG19 model with 93.5%. In addition, increases in accuracy were noted in all models with the use of the support vector machine classifier.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Conclusion:<\/jats:bold>\n                    Differentiation of benign and malignant lung nodules using deep learning models and feature extraction will provide important advantages for early diagnosis in radiology practice. The results obtained in our study support this view.\n                  <\/jats:p>","DOI":"10.5606\/tgkdc.dergisi.2024.26027","type":"journal-article","created":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T12:03:52Z","timestamp":1726142632000},"page":"317-324","source":"Crossref","is-referenced-by-count":1,"title":["Deep learning in distinguishing pulmonary nodules as benign and malignant"],"prefix":"10.4274","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2174-962X","authenticated-orcid":false,"given":"Muhammed","family":"Bilal Ak\u0131nc\u0131","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3095-2446","authenticated-orcid":false,"given":"Mesut","family":"\u00d6zg\u00f6k\u00e7e","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8120-5101","authenticated-orcid":false,"given":"Murat","family":"Canayaz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3089-7165","authenticated-orcid":false,"given":"Fatma","family":"Durmaz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9245-0206","authenticated-orcid":false,"given":"Sercan","family":"\u00d6zka\u00e7maz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1429-077X","authenticated-orcid":false,"given":"\u0130lyas","family":"D\u00fcndar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7989-5668","authenticated-orcid":false,"given":"Ensar","family":"T\u00fcrko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4792-8722","authenticated-orcid":false,"given":"Cemil","family":"G\u00f6ya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2811","published-online":{"date-parts":[[2024,7,23]]},"reference":[{"key":"ref-1","doi-asserted-by":"crossref","unstructured":"Thandra KC, Barsouk A, Saginala K, Aluru JS, Barsouk A. Epidemiology of lung cancer. Contemp Oncol (Pozn) 2021;25:45-52. doi: 10.5114\/wo.2021.103829.","DOI":"10.5114\/wo.2021.103829"},{"key":"ref-2","unstructured":"Collins LG, Haines C, Perkel R, Enck RE. Lung cancer: Diagnosis and management. Am Fam Physician 2007;75:56-63."},{"key":"ref-3","doi-asserted-by":"crossref","unstructured":"Tanoue LT, Tanner NT, Gould MK, Silvestri GA. Lung cancer screening. Am J Respir Crit Care Med 2015;191:19-33. doi: 10.1164\/rccm.201410-1777CI.","DOI":"10.1164\/rccm.201410-1777CI"},{"key":"ref-4","doi-asserted-by":"crossref","unstructured":"Gon\u00e7alves L, Novo J, Cunha A, Campilho A. Learning lung nodule malignancy likelihood from radiologist annotations or diagnosis data. J Med Biol Eng 2018;38:424-42. doi: 10.1007\/s40846-017-0317-2.","DOI":"10.1007\/s40846-017-0317-2"},{"key":"ref-5","unstructured":"Kartalo\u011flu Z. Soliter pulmoner nod\u00fcle yakla\u015f\u0131m. Turkish J Thorac Cardiovasc Surg 2008;16:274-83."},{"key":"ref-6","doi-asserted-by":"crossref","unstructured":"Ost D, Fein AM, Feinsilver SH. Clinical practice. The solitary pulmonary nodule. N Engl J Med 2003;348:2535-42. doi: 10.1056\/NEJMcp012290.","DOI":"10.1056\/NEJMcp012290"},{"key":"ref-7","doi-asserted-by":"crossref","unstructured":"Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer 2018;18:500-10. doi: 10.1038\/s41568-018-0016-5.","DOI":"10.1038\/s41568-018-0016-5"},{"key":"ref-8","doi-asserted-by":"crossref","unstructured":"Soubani AO. The evaluation and management of the solitary pulmonary nodule. Postgrad Med J 2008;84:459-66. doi: 10.1136\/pgmj.2007.063545.","DOI":"10.1136\/pgmj.2007.063545"},{"key":"ref-9","doi-asserted-by":"crossref","unstructured":"Kim RY, Oke JL, Pickup LC, Munden RF, Dotson TL, Bellinger CR, et al. Artificial intelligence tool for assessment of indeterminate pulmonary nodules detected with CT. Radiology 2022;304:683-91. doi: 10.1148\/radiol.212182.","DOI":"10.1148\/radiol.212182"},{"key":"ref-10","doi-asserted-by":"crossref","unstructured":"da N\u00f3brega RVM, Rebou\u00e7as Filho PP, Rodrigues MB, da Silva SPP, Dourado J\u00fanior CMJM, de Albuquerque VHC. Lung nodule malignancy classification in chest computed tomography images using transfer learning and convolutional neural networks. Neural Comput Applic 2020;32:11065-82. doi: 10.1007\/s00521-018-3895-1.","DOI":"10.1007\/s00521-018-3895-1"},{"key":"ref-11","doi-asserted-by":"crossref","unstructured":"Monkam P, Qi S, Ma H, Gao W, Yao Y, Qian W. Detection and classification of pulmonary nodules using convolutional neural networks: A survey. IEEE Access 2019;7:78075-91.","DOI":"10.1109\/ACCESS.2019.2920980"},{"key":"ref-12","doi-asserted-by":"crossref","unstructured":"Sun W, Zheng B, Qian W. Computer aided lung cancer diagnosis with deep learning algorithms. Presented at the SPIE medical imaging,Computer-Aided Diagnosis; 2016 Feb 27-3, San diego. 2016;9785:241-8. doi: 10.1117\/12.2216307","DOI":"10.1117\/12.2216307"},{"key":"ref-13","doi-asserted-by":"crossref","unstructured":"Hussein S, Gillies R, Cao K, Song Q, Bagci U. TumorNet: Lung nodule characterization using multi-view Convolutional Neural Network with Gaussian Process 2017:1007-10. doi: 10.1109\/ISBI.2017.7950686.","DOI":"10.1109\/ISBI.2017.7950686"},{"key":"ref-14","doi-asserted-by":"crossref","unstructured":"Nibali A, He Z, Wollersheim D. Pulmonary nodule classification with deep residual networks. Int J Comput Assist Radiol Surg 2017;12:1799-808. doi: 10.1007\/s11548- 017-1605-6.","DOI":"10.1007\/s11548-017-1605-6"},{"key":"ref-15","doi-asserted-by":"crossref","unstructured":"Dai Y, Yan S, Zheng B, Song C. Incorporating automatically learned pulmonary nodule attributes into a convolutional neural network to improve accuracy of benign-malignant nodule classification. Phys Med Biol 2018;63:245004. doi: 10.1088\/1361-6560\/aaf09f.","DOI":"10.1088\/1361-6560\/aaf09f"},{"key":"ref-16","doi-asserted-by":"crossref","unstructured":"Krizhevsky A, Sutskever I, Hinton G. ImageNet classification with deep convolutional neural networks. NeurIPS 2012:25. doi: 10.1145\/3065386.","DOI":"10.1145\/3065386"},{"key":"ref-17","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al. Going deeper with convolutions. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015:1-9. doi: 10.1109\/CVPR.2015.7298594.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref-18","doi-asserted-by":"crossref","unstructured":"Shen W, Zhou M, Yang F, Yu D, Dong D, Yang C, et al. Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification. Pattern Recognit 2017;61:663-73. doi: 10.1016\/j.patcog.2016.05.029.","DOI":"10.1016\/j.patcog.2016.05.029"}],"container-title":["Turkish Journal of Thoracic and Cardiovascular Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/tgkdc.dergisi.org\/uploads\/pdf\/pdf_TGKDC_3809.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/tgkdc.dergisi.org\/uploads\/pdf\/pdf_TGKDC_3809.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T13:52:31Z","timestamp":1782222751000},"score":1,"resource":{"primary":{"URL":"https:\/\/tjtcvs.org\/articles\/doi\/tgkdc.dergisi.2024.26027"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,1]]},"references-count":18,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,7,1]]}},"URL":"https:\/\/doi.org\/10.5606\/tgkdc.dergisi.2024.26027","relation":{},"ISSN":["1301-5680","2149-8156"],"issn-type":[{"value":"1301-5680","type":"print"},{"value":"2149-8156","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,1]]}}}