{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T07:22:45Z","timestamp":1767165765682,"version":"build-2238731810"},"reference-count":45,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T00:00:00Z","timestamp":1660089600000},"content-version":"vor","delay-in-days":221,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002383","name":"King Saud University","doi-asserted-by":"publisher","award":["RSP-2022\/230"],"award-info":[{"award-number":["RSP-2022\/230"]}],"id":[{"id":"10.13039\/501100002383","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BioMed Research International"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:p>The second largest cause of mortality worldwide is breast cancer, and it mostly occurs in women. Early diagnosis has improved further treatments and reduced the level of mortality. A unique deep learning algorithm is presented for predicting breast cancer in its early stages. This method utilizes numerous layers to retrieve significantly greater amounts of information from the source inputs. It could perform automatic quantitative evaluation of complicated image properties in the medical field and give greater precision and reliability during the diagnosis. The dataset of axillary lymph nodes from the breast cancer patients was collected from Erasmus Medical Center. A total of 1050 images were studied from the 850 patients during the years 2018 to 2021. For the independent test, data samples were collected for 100 images from 95 patients at national cancer institute. The existence of axillary lymph nodes was confirmed by pathologic examination. The feed forward, radial basis function, and Kohonen self\u2010organizing are the artificial neural networks (ANNs) which are used to train 84% of the Erasmus Medical Center dataset and test the remaining 16% of the independent dataset. The proposed model performance was determined in terms of accuracy (Ac), sensitivity (Sn), specificity (Sf), and the outcome of the receiver operating curve (Roc), which was compared to the other four radiologists\u2019 mechanism. The result of the study shows that the proposed mechanism achieves 95% sensitivity, 96% specificity, and 98% accuracy, which is higher than the radiologists\u2019 models (90% sensitivity, 92% specificity, and 94% accuracy). Deep learning algorithms could accurately predict the clinical negativity of axillary lymph node metastases by utilizing images of initial breast cancer patients. This method provides an earlier diagnostic technique for axillary lymph node metastases in patients with medically negative changes in axillary lymph nodes.<\/jats:p>","DOI":"10.1155\/2022\/8616535","type":"journal-article","created":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T20:35:12Z","timestamp":1660163712000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["[Retracted] Deep Learning Mechanism for Predicting the Axillary Lymph Node Metastasis in Patients with Primary Breast Cancer"],"prefix":"10.1155","volume":"2022","author":[{"given":"N.","family":"Ashokkumar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S.","family":"Meera","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"P.","family":"Anandan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mantripragada Yaswanth Bhanu","family":"Murthy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K. S.","family":"Kalaivani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tahani Awad","family":"Alahmadi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sulaiman Ali","family":"Alharbi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S. S.","family":"Raghavan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3698-9549","authenticated-orcid":false,"given":"S. Arockia","family":"Jayadhas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,8,10]]},"reference":[{"key":"e_1_2_10_1_2","first-page":"315","article-title":"Diagnosis and prognosis breast cancer using classification rules","volume":"2","author":"Joshi J.","year":"2014","journal-title":"International Journal of Engineering Research and General Science"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrc1670"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.suc.2018.04.001"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1177\/107327481001700302"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1186\/1746-1596-6-18"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1093\/annonc\/mdv298"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa0904832"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1245\/s10434-011-1957-z"},{"key":"e_1_2_10_9_2","first-page":"3581","article-title":"Significance of lymphatic invasion combined with size of primary tumor for predicting sentinel lymph node metastasis in patients with breast cancer","volume":"35","author":"Fujii T.","year":"2015","journal-title":"Anticancer Research"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-013-2930-y"},{"key":"e_1_2_10_11_2","doi-asserted-by":"crossref","unstructured":"AhmadA. M. KhanG. M. MahmudS. A. andMillerJ. F. Breast cancer detection using Cartesian genetic programming evolved artificial neural networks Proceedings of the 14th annual conference on Genetic and evolutionary computation 2012 Philadelphia Pennsylvania USA 1031\u20131038.","DOI":"10.1145\/2330163.2330307"},{"key":"e_1_2_10_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-012-1134-8"},{"key":"e_1_2_10_13_2","doi-asserted-by":"publisher","DOI":"10.7326\/0003-4819-148-9-200805060-00007"},{"key":"e_1_2_10_14_2","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/4352730"},{"key":"e_1_2_10_15_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41568-018-0016-5"},{"key":"e_1_2_10_16_2","doi-asserted-by":"publisher","DOI":"10.4066\/biomedicalresearch.29-18-886"},{"key":"e_1_2_10_17_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5709257"},{"key":"e_1_2_10_18_2","article-title":"An efficient prediction of breast cancer data using data mining techniques","volume":"2","author":"Kumar G. R.","year":"2013","journal-title":"International Journal of Innovations in Engineering and Technology (IJIET)"},{"key":"e_1_2_10_19_2","doi-asserted-by":"publisher","DOI":"10.1177\/117693510600200030"},{"key":"e_1_2_10_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0933-3657(00)00110-X"},{"key":"e_1_2_10_21_2","volume-title":"Data Mining: Concepts and Techniques","author":"Han J.","year":"2011"},{"key":"e_1_2_10_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2004.07.002"},{"key":"e_1_2_10_23_2","doi-asserted-by":"crossref","unstructured":"IslamM. M. IqbalH. HaqueM. R. andHasanM. K. Prediction of breast cancer using support vector machine and K-nearest neighbors 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC) 2017 Dhaka Bangladesh 226\u2013229.","DOI":"10.1109\/R10-HTC.2017.8288944"},{"key":"e_1_2_10_24_2","doi-asserted-by":"crossref","unstructured":"KumarU. K. NikhilM. S. andSumangaliK. Prediction of breast cancer using voting classifier technique 2017 IEEE international conference on smart technologies and management for computing communication controls energy and materials (ICSTM) 2017 Chennai India 108\u2013114.","DOI":"10.1109\/ICSTM.2017.8089135"},{"key":"e_1_2_10_25_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btx487"},{"key":"e_1_2_10_26_2","doi-asserted-by":"publisher","DOI":"10.1038\/bjc.1996.563"},{"key":"e_1_2_10_27_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-009-9427-x"},{"key":"e_1_2_10_28_2","doi-asserted-by":"publisher","DOI":"10.14419\/ijet.v7i4.20.22115"},{"key":"e_1_2_10_29_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btu128"},{"key":"e_1_2_10_30_2","doi-asserted-by":"publisher","DOI":"10.1136\/jcp.2006.041830"},{"key":"e_1_2_10_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbe.2019.03.001"},{"key":"e_1_2_10_32_2","doi-asserted-by":"publisher","DOI":"10.1186\/bcr3375"},{"key":"e_1_2_10_33_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13058-014-0456-0"},{"key":"e_1_2_10_34_2","article-title":"Breast cancer diagnosis and survival prediction using JNN","volume":"4","author":"Shawarib M. Z. A.","year":"2020","journal-title":"International Journal of Engineering and Information Systems (IJEAIS)"},{"key":"e_1_2_10_35_2","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2008.18.1370"},{"key":"e_1_2_10_36_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12864-018-4527-y"},{"key":"e_1_2_10_37_2","doi-asserted-by":"publisher","DOI":"10.1177\/1176935118810215"},{"key":"e_1_2_10_38_2","doi-asserted-by":"publisher","DOI":"10.1093\/annonc\/12.suppl_1.S23"},{"key":"e_1_2_10_39_2","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky A.","year":"2012","journal-title":"Advances in neural information processing systems"},{"key":"e_1_2_10_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2015.2482920"},{"key":"e_1_2_10_41_2","doi-asserted-by":"publisher","DOI":"10.1245\/s10434-013-3110-7"},{"key":"e_1_2_10_42_2","doi-asserted-by":"publisher","DOI":"10.1055\/s-0035-1555872"},{"key":"e_1_2_10_43_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-018-0144-1"},{"key":"e_1_2_10_44_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-011-9207-8"},{"key":"e_1_2_10_45_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamaoncol.2017.3625"}],"updated-by":[{"DOI":"10.1155\/2023\/9867095","type":"retraction","label":"Retraction","source":"retraction-watch","updated":{"date-parts":[[2023,12,29]],"date-time":"2023-12-29T00:00:00Z","timestamp":1703808000000},"record-id":"57978"},{"DOI":"10.1155\/2023\/9867095","type":"retraction","label":"Retraction","source":"publisher","updated":{"date-parts":[[2023,12,29]],"date-time":"2023-12-29T00:00:00Z","timestamp":1703808000000}}],"container-title":["BioMed Research International"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/bmri\/2022\/8616535.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/bmri\/2022\/8616535.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2022\/8616535","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T02:54:09Z","timestamp":1735613649000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2022\/8616535"}},"subtitle":[],"editor":[{"given":"Yuvaraja","family":"Teekaraman","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2022,1]]},"references-count":45,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["10.1155\/2022\/8616535"],"URL":"https:\/\/doi.org\/10.1155\/2022\/8616535","archive":["Portico"],"relation":{},"ISSN":["2314-6133","2314-6141"],"issn-type":[{"value":"2314-6133","type":"print"},{"value":"2314-6141","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1]]},"assertion":[{"value":"2022-04-07","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-07-15","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-08-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"8616535"}}