{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T20:46:16Z","timestamp":1779137176763,"version":"3.51.4"},"reference-count":15,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T00:00:00Z","timestamp":1641772800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T00:00:00Z","timestamp":1641772800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Res Notes"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Objective<\/jats:title>\n                <jats:p>Breast cancer is the most common among women, and it causes many deaths every year. Early diagnosis increases the chance of cure through treatment. The traditional manual diagnosis requires effort and time from pathological experts, as it needs a joint experience of a number of pathologists. Diagnostic mistakes can lead to catastrophic results and endanger the lives of patients. The presence of an expert system that is able to specify whether the examined tissue is healthy or not, thus improves the quality of diagnosis and saves the time of experts. In this paper, a model capable of classifying breast cancer anatomy by making use of a pre-trained DCNN has been proposed. To build this model, first of all the image should be color stained by using Vahadane algorithm, then the model which combines three pre-trained DCNN (Xception, NASNet and Inceptoin_Resnet_V2) should be built in parallel, then the three branches should be aggregated to take advantage of each other. The suggested model was tested under different values of threshold ratios and also compared with other models.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The proposed model on the BreaKHis dataset achieved 98% accuracy, which is better than the accuracy of other models used in this field.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s13104-021-05902-3","type":"journal-article","created":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T14:02:51Z","timestamp":1641823371000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Using parallel pre-trained types of DCNN model to predict breast cancer with color normalization"],"prefix":"10.1186","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9074-6230","authenticated-orcid":false,"given":"William","family":"Al Noumah","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Assef","family":"Jafar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kadan","family":"Al Joumaa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,10]]},"reference":[{"issue":"1","key":"5902_CR1","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/s12530-019-09297-2","volume":"11","author":"TG Debelee","year":"2020","unstructured":"Debelee TG, Schwenker F, Ibenthal A, Yohannes D. Survey of deep learning in breast cancer image analysis. Evol Syst. 2020;11(1):143\u201363.","journal-title":"Evol Syst"},{"key":"5902_CR2","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.csbj.2018.01.001","volume":"16","author":"D Komura","year":"2018","unstructured":"Komura D, Ishikawa S. Machine learning methods for histopathological image analysis. Comput Struct Biotechnol J. 2018;16:34\u201342.","journal-title":"Comput Struct Biotechnol J"},{"issue":"1","key":"5902_CR3","doi-asserted-by":"publisher","first-page":"353","DOI":"10.13005\/bpj\/1116","volume":"10","author":"KK Shukla","year":"2017","unstructured":"Shukla KK, Tiwari A, Sharma S. Classification of histopathological images of breast cancerous and non cancerous cells based on morphological features. Biomed Pharmacol J. 2017;10(1):353\u201366.","journal-title":"Biomed Pharmacol J"},{"key":"5902_CR4","doi-asserted-by":"publisher","unstructured":"Street WN, Wolberg WH, Mangasarian OL. Nuclear feature extraction for breast tumor diagnosis. In: Biomedical image processing and biomedical visualization. International Society for Optics and Photonics; 1993. vol. 1905, p. 861\u2013870.  https:\/\/doi.org\/10.1117\/12.148698.","DOI":"10.1117\/12.148698"},{"key":"5902_CR5","unstructured":"Dua D, Graff C. UCI machine learning repository. Irvine, CA: University of California, School of Information and Computer Science; 2019. http:\/\/archive.ics.uci.edu\/ml."},{"key":"5902_CR6","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1177\/117693510600200030","volume":"2","author":"JA Cruz","year":"2006","unstructured":"Cruz JA, Wishart DS. Applications of machine learning in cancer prediction and prognosis. Cancer Inform. 2006;2:59\u201377.","journal-title":"Cancer Inform"},{"key":"5902_CR7","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.csbj.2014.11.005","volume":"13","author":"K Kourou","year":"2015","unstructured":"Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J. 2015;13:8\u201317.","journal-title":"Comput Struct Biotechnol J"},{"key":"5902_CR8","doi-asserted-by":"publisher","DOI":"10.1177\/10104283176945500","author":"JM Chen","year":"2017","unstructured":"Chen JM, Li Y, Xu J, Gong L, Wang LW, Liu WL, Liu J. Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: a review. Tumor Biol. 2017. https:\/\/doi.org\/10.1177\/10104283176945500.","journal-title":"Tumor Biol"},{"key":"5902_CR9","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.patcog.2018.05.014","volume":"83","author":"Z Hu","year":"2018","unstructured":"Hu Z, Tang J, Wang Z, Zhang K, Zhang L, Sun Q. Deep learning for image-based cancer detection and diagnosis\u2014a survey. Pattern Recognit. 2018;83:134\u201349.","journal-title":"Pattern Recognit"},{"key":"5902_CR10","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1590\/1517-3151.0517","volume":"30","author":"VM Gon\u00e7alves","year":"2014","unstructured":"Gon\u00e7alves VM, Delamaro ME, Nunes FD. A systematic review on the evaluation and characteristics of computer-aided diagnosis systems. Rev Bras Eng Biom\u00e9d. 2014;30:355\u201383.","journal-title":"Rev Bras Eng Biom\u00e9d"},{"issue":"3","key":"5902_CR11","doi-asserted-by":"publisher","first-page":"47","DOI":"10.3390\/mti2030047","volume":"2","author":"M Bakator","year":"2018","unstructured":"Bakator M, Radosav D. Deep learning and medical diagnosis: a review of literature. Multimodal Technol Interact. 2018;2(3):47.","journal-title":"Multimodal Technol Interact"},{"key":"5902_CR12","volume-title":"Machine learning and big data analytics paradigms: analysis, applications and challenges","author":"AE Hassanien","year":"2021","unstructured":"Hassanien AE, Darwish A. Machine learning and big data analytics paradigms: analysis, applications and challenges. Cham: Springer; 2021."},{"issue":"7","key":"5902_CR13","doi-asserted-by":"publisher","first-page":"1455","DOI":"10.1109\/TBME.2015.2496264","volume":"63","author":"FA Spanhol","year":"2016","unstructured":"Spanhol FA, Oliveira LS, Petitjean C, Heutte L. A dataset for breast cancer histopathological image classification. IEEE Trans Biomed Eng. 2016;63(7):1455\u201362.","journal-title":"IEEE Trans Biomed Eng"},{"key":"5902_CR14","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2529665","author":"A Vahadane","year":"2016","unstructured":"Vahadane A, Peng T, Sethi A, Albarqouni S, Wang L, Baust M, Steiger K, Schlitter AM, Esposito I, Navab N. Structure-preserving color normalization and sparse stain separation for histological images. IEEE Trans Med Imaging. 2016. https:\/\/doi.org\/10.1109\/TMI.2016.2529665.","journal-title":"IEEE Trans Med Imaging"},{"key":"5902_CR15","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks (2018). arXiv:1608.06993v5.","DOI":"10.1109\/CVPR.2017.243"}],"container-title":["BMC Research Notes"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13104-021-05902-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13104-021-05902-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13104-021-05902-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T14:15:38Z","timestamp":1641824138000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcresnotes.biomedcentral.com\/articles\/10.1186\/s13104-021-05902-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,10]]},"references-count":15,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["5902"],"URL":"https:\/\/doi.org\/10.1186\/s13104-021-05902-3","relation":{},"ISSN":["1756-0500"],"issn-type":[{"value":"1756-0500","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,10]]},"assertion":[{"value":"17 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 December 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study is based on a public dataset, and the BreaKHis dataset is allowed for academic use.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"14"}}