{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T10:30:06Z","timestamp":1771065006702,"version":"3.50.1"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2023,7,9]],"date-time":"2023-07-09T00:00:00Z","timestamp":1688860800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,9]],"date-time":"2023-07-09T00:00:00Z","timestamp":1688860800000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s00521-023-08771-y","type":"journal-article","created":{"date-parts":[[2023,7,9]],"date-time":"2023-07-09T06:01:40Z","timestamp":1688882500000},"page":"7641-7655","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Denoising histopathology images for the detection of breast cancer"],"prefix":"10.1007","volume":"37","author":[{"given":"Muhammad Haider","family":"Zeb","sequence":"first","affiliation":[]},{"given":"Feras","family":"Al-Obeidat","sequence":"additional","affiliation":[]},{"given":"Abdallah","family":"Tubaishat","sequence":"additional","affiliation":[]},{"given":"Fawad","family":"Qayum","sequence":"additional","affiliation":[]},{"given":"Ahsan","family":"Fazeel","sequence":"additional","affiliation":[]},{"given":"Muhammad","family":"Amin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,9]]},"reference":[{"key":"8771_CR1","volume-title":"Cancer facts and figures","author":"American Cancer Society","year":"2023","unstructured":"American Cancer Society (2023) Cancer facts and figures. Atlanta, GA"},{"issue":"5","key":"8771_CR2","doi-asserted-by":"crossref","first-page":"pdb-rot4986","DOI":"10.1101\/pdb.prot4986","volume":"2008","author":"AH Fischer","year":"2008","unstructured":"Fischer AH, Jacobson KA, Rose J, Zeller R (2008) Hematoxylin and eosin staining of tissue and cell sections. Cold Spring Harb Protoc 2008(5):pdb-rot4986","journal-title":"Cold Spring Harb Protoc"},{"issue":"3","key":"8771_CR3","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1007\/s10278-019-00307-y","volume":"33","author":"S Sharma","year":"2020","unstructured":"Sharma S, Mehra Rajesh (2020) Conventional machine learning and deep learning approach for multi-classification of breast cancer histopathology images-a comparative insight. J Dig Imaging 33(3):632\u2013654","journal-title":"J Dig Imaging"},{"issue":"16","key":"8771_CR4","doi-asserted-by":"crossref","first-page":"4373","DOI":"10.3390\/s20164373","volume":"20","author":"Z Hameed","year":"2020","unstructured":"Hameed Z, Zahia S, Garcia-Zapirain B, Aguirre JJ, Vanegas AM (2020) Breast cancer histopathology image classification using an ensemble of deep learning models. Sensors 20(16):4373","journal-title":"Sensors"},{"key":"8771_CR5","doi-asserted-by":"crossref","DOI":"10.1016\/j.physa.2019.123592","volume":"545","author":"M To\u01e7a\u00e7ar","year":"2020","unstructured":"To\u01e7a\u00e7ar M, \u00d6zkurt KB, Ergen B, C\u00f6mert Z (2020) Breastnet: a novel convolutional neural network model through histopathological images for the diagnosis of breast cancer. Phys A Stat Mech Appl 545:123592","journal-title":"Phys A Stat Mech Appl"},{"key":"8771_CR6","doi-asserted-by":"crossref","unstructured":"Pan X, Li L, Yang H, Liu Z, He Y, Li Z, Fan Y, Cao Z, Zhang L (2019) Multi-task deep learning for fine-grained classification\/grading in breast cancer histopathological images. In: Cognitive internet of things","DOI":"10.1007\/978-3-030-04946-1_10"},{"key":"8771_CR7","doi-asserted-by":"crossref","first-page":"23203","DOI":"10.1016\/j.patrec.2020.03.011","volume":"133","author":"Y Celik","year":"2020","unstructured":"Celik Y, Talo M, Y\u0131ld\u0131r\u0131m \u00d6, Karabatak M, Acharya UR (2020) Automated invasive ductal carcinoma detection based using deep transfer learning with whole-slide images. Pattern Recognit Lett 133:23203","journal-title":"Pattern Recognit Lett"},{"key":"8771_CR8","doi-asserted-by":"crossref","first-page":"80","DOI":"10.3389\/fgene.2019.00080","volume":"10","author":"J Xie","year":"2019","unstructured":"Xie J, Liu Ran, Luttrell J, Zhang C (2019) Deep learning based analysis of histopathological images of breast cancer. Front Genet 10:80","journal-title":"Front Genet"},{"key":"8771_CR9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.patrec.2019.03.022","volume":"125","author":"I van Din","year":"2019","unstructured":"van Din I, Rodrigues J, Islam N (2019) A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recognit Lett 125:1\u20136","journal-title":"Pattern Recognit Lett"},{"key":"8771_CR10","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1007\/s10278-019-00182-7","volume":"32","author":"MZ Alom","year":"2018","unstructured":"Alom MZ, Yakopcic C, Taha TM, Asari VK (2018) Breast cancer classification from histopathological images with inception recurrent residual convolutional neural network. J Dig Imaging 32:605","journal-title":"J Dig Imaging"},{"key":"8771_CR11","first-page":"12","volume":"71","author":"K Roy","year":"2018","unstructured":"Roy K, Banik Debapriya, Bhattacharjee D, Nasipuri M (2018) Patch-based system for classification of breast histology images using deep learning. Comput Med Imaging Graph 71:12","journal-title":"Comput Med Imaging Graph"},{"key":"8771_CR12","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.ins.2018.12.089","volume":"482","author":"D Vo","year":"2018","unstructured":"Vo D, Nguyen Q (2018) Classification of breast cancer histology images using incremental boosting convolution networks. Inf Sci 482:123\u2013138","journal-title":"Inf Sci"},{"key":"8771_CR13","doi-asserted-by":"crossref","unstructured":"Nawaz W, Ahmed S, Tahir A, Khan HA (2018) Classification of breast cancer histology images using alexnet. In: Image analysis and recognition: 15th international conference, ICIAR 2018, P\u00f3voa de Varzim, Portugal, June 27\u201329, 2018, Proceedings 15. Springer International Publishing, pp 869\u2013876","DOI":"10.1007\/978-3-319-93000-8_99"},{"issue":"03","key":"8771_CR14","first-page":"2018","volume":"1\u201320","author":"A Nahid","year":"2018","unstructured":"Nahid A, Mehrabi Ali, Kong Y (2018) Histopathological breast cancer image classification by deep neural network techniques guided by local clustering. BioMed Res Int 1\u201320(03):2018","journal-title":"BioMed Res Int"},{"key":"8771_CR15","first-page":"06","volume":"173","author":"R Yan","year":"2019","unstructured":"Yan R, Ren F, Zihao W, Wang L, Zhang T, Liu Y, Rao X, Zheng C, Zhang F (2019) Breast cancer histopathological image classification using a hybrid deep neural network. Methods 173:06","journal-title":"Methods"},{"key":"8771_CR16","doi-asserted-by":"crossref","first-page":"06","DOI":"10.1038\/s41598-017-00059-1","volume":"7","author":"Z Han","year":"2017","unstructured":"Han Z, Wei B, Zheng Y, Yin Y, Li K, Li Shuo (2017) Breast cancer multi-classification from histopathological images with structured deep learning model. Sci Rep 7:06","journal-title":"Sci Rep"},{"key":"8771_CR17","doi-asserted-by":"crossref","unstructured":"Wei B, Han Z, He X, Yin Y (2017) Deep learning model based breast cancer histopathological image classification. In 2017 IEEE 2nd international conference on cloud computing and big data analysis (ICCCBDA). IEEE, pp 348\u2013353","DOI":"10.1109\/ICCCBDA.2017.7951937"},{"key":"8771_CR18","doi-asserted-by":"crossref","unstructured":"Bayramoglu N, Kannala J, Heikkila J (2016) Deep learning for magnification independent breast cancer histopathology image classification. 12: 2440\u20132445","DOI":"10.1109\/ICPR.2016.7900002"},{"key":"8771_CR19","doi-asserted-by":"crossref","unstructured":"Kwok S (2018) Multiclass classification of breast cancer in whole-slide images. In: Image analysis and recognition: 15th international conference, ICIAR 2018, P\u00f3voa de Varzim, Portugal, June 27\u201329, 2018, Proceedings 15. Springer International Publishing, pp 931\u2013940","DOI":"10.1007\/978-3-319-93000-8_106"},{"key":"8771_CR20","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.eswa.2018.09.049","volume":"117","author":"PJ Sudharshan","year":"2018","unstructured":"Sudharshan PJ, Petitjean C, Spanhol F, Oliveira L, Heutte L, Honeine P (2018) Multiple instance learning for histopathological breast cancer image classification. Expert Syst Appl 117:103","journal-title":"Expert Syst Appl"},{"issue":"662","key":"8771_CR21","first-page":"09","volume":"10","author":"C-J Lin","year":"2020","unstructured":"Lin C-J, Shiou-Yun Jeng (2020) Optimization of deep learning network parameters using uniform experimental design for breast cancer histopathological image classification. Diagnostics 10(662):09","journal-title":"Diagnostics"},{"issue":"6","key":"8771_CR22","doi-asserted-by":"crossref","first-page":"e0177544","DOI":"10.1371\/journal.pone.0177544","volume":"12","author":"T Ara\u00fajo","year":"2017","unstructured":"Ara\u00fajo T et al (2017) Classification of breast cancer histology images using convolutional neural networks. PloS one 12(6):e0177544","journal-title":"PloS one"},{"key":"8771_CR23","volume-title":"UCI machine learning repository Irvine","author":"D Dua","year":"2019","unstructured":"Dua D, Graff C (2019) UCI machine learning repository Irvine. University of California, School of Information and Computer Science, CA"},{"key":"8771_CR24","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.1109\/TBME.2015.2496264","volume":"63","author":"F Spanhol","year":"2015","unstructured":"Spanhol F, de Oliveira LS, Petitjean C, Heutte L (2015) A dataset for breast cancer histopathological image classification. IEEE Trans Bio-med Eng 63:1455","journal-title":"IEEE Trans Bio-med Eng"},{"key":"8771_CR25","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1007\/s10586-017-1010-5","volume":"21","author":"M Amin","year":"2018","unstructured":"Amin M, Tanveer TA, Shah S, Abdullah M, Shafi Muhammad (2018) DeepSIC: a deep model: For satellite image classification. Cluster Computing 21:741\u2013754","journal-title":"Cluster Computing"},{"key":"8771_CR26","doi-asserted-by":"publisher","unstructured":"Zuluaga FHG, Bardozzo F, Patino JIR, Tagliaferri R (2021) Blind microscopy image denoising with a deep residual and multiscale encoder\/decoder network. In: 43rd annual international conference of the IEEE engineering in medicine & biology society (EMBC), Mexico, pp 3483\u20133486. https:\/\/doi.org\/10.1109\/EMBC46164.2021.9630502","DOI":"10.1109\/EMBC46164.2021.9630502"},{"issue":"4","key":"8771_CR27","volume":"19","author":"S Prusty","year":"2022","unstructured":"Prusty S, Patnaik S, Dash SK (2022) SKCV: stratified K-fold cross-validation on ML classifiers for predicting cervical cancer. Front Nanotechnol 19(4):972421","journal-title":"Front Nanotechnol"},{"issue":"2020","key":"8771_CR28","volume":"545","author":"M To\u01e7a\u00e7ar","year":"2020","unstructured":"To\u01e7a\u00e7ar M, \u00d6zkurt KB, Ergen B, C\u00f6mert Z (2020) BreastNet: a novel convolutional neural network model through histopathological images for the diagnosis of breast cancer. Phys A Stat Mech Appl 545(2020):123592","journal-title":"Phys A Stat Mech Appl"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08771-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08771-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08771-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T17:48:02Z","timestamp":1742665682000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08771-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,9]]},"references-count":28,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["8771"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08771-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,9]]},"assertion":[{"value":"26 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 June 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 July 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflicts of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}