{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T13:32:55Z","timestamp":1782135175109,"version":"3.54.5"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2021,7,24]],"date-time":"2021-07-24T00:00:00Z","timestamp":1627084800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,7,24]],"date-time":"2021-07-24T00:00:00Z","timestamp":1627084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2021,9]]},"DOI":"10.1007\/s11517-021-02403-0","type":"journal-article","created":{"date-parts":[[2021,7,24]],"date-time":"2021-07-24T00:02:37Z","timestamp":1627084957000},"page":"1773-1783","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["An SVM approach towards breast cancer classification from H&amp;E-stained histopathology images based on integrated features"],"prefix":"10.1007","volume":"59","author":[{"given":"M. A.","family":"Aswathy","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M.","family":"Jagannath","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,7,24]]},"reference":[{"issue":"11","key":"2403_CR1","doi-asserted-by":"publisher","first-page":"e00938","DOI":"10.1016\/j.heliyon.2018.e00938","volume":"4","author":"OI Abiodun","year":"2018","unstructured":"Abiodun OI, Jantan A, Omolara AE, Dada KV, Mohamed NA, Arshad H (2018) State-of-the-art in artificial neural network applications: a survey. Heliyon 4(11):e00938","journal-title":"Heliyon"},{"issue":"1","key":"2403_CR2","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1111\/joim.13030","volume":"288","author":"B Acs","year":"2020","unstructured":"Acs B, Rantalainen M, Hartman J (2020) Artificial intelligence as the next step towards precision pathology. J Intern Med 288(1):62\u201381","journal-title":"J Intern Med"},{"key":"2403_CR3","doi-asserted-by":"publisher","first-page":"2357","DOI":"10.1007\/s10639-019-09873-8","volume":"24","author":"S Al-Sudani","year":"2019","unstructured":"Al-Sudani S, Palaniappan R (2019) Predicting students\u2019 final degree classification using an extended profile. Educ Inf Technol 24:2357\u20132369","journal-title":"Educ Inf Technol"},{"key":"2403_CR4","unstructured":"American Cancer Society, Cancer facts and figures 2018, ACS, Atlanta; 2018. https:\/\/www.cancer.org\/research\/cancer-facts-statistics\/all-cancer-facts-figures\/cancer-facts-figures-2018.html"},{"issue":"1","key":"2403_CR5","doi-asserted-by":"publisher","first-page":"353","DOI":"10.13005\/bpj\/1116","volume":"10","author":"A Anuranjeeta","year":"2017","unstructured":"Anuranjeeta A, Shukla KK, Tiwari A, Sharma S (2017) Classification of histopathological images of breast cancerous and non-cancerous cells based on morphological features. Biomed Pharmacol J 10(1):353\u2013366","journal-title":"Biomed Pharmacol J"},{"issue":"3","key":"2403_CR6","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.1016\/j.procs.2016.04.224","volume":"8","author":"H Asri","year":"2016","unstructured":"Asri H, Mousannif H, Al M, Noel T (2016) Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Comput Sci 8(3):1064\u20131069","journal-title":"Procedia Comput Sci"},{"key":"2403_CR7","doi-asserted-by":"publisher","first-page":"666","DOI":"10.1016\/j.procs.2020.03.333","volume":"167","author":"MA Aswathy","year":"2020","unstructured":"Aswathy MA, Jagannath M (2020) Performance analysis of segmentation algorithms for the detection of breast cancer. Procedia Comput Sci 167:666\u2013676","journal-title":"Procedia Comput Sci"},{"issue":"7","key":"2403_CR8","doi-asserted-by":"publisher","first-page":"941","DOI":"10.1016\/j.joca.2020.03.006","volume":"28","author":"N Bayramoglu","year":"2020","unstructured":"Bayramoglu N, Tiulpin A, Hirvasniemi J, Nieminen MT, Saarakkala S (2020) Adaptive segmentation of knee radiographs for selecting the optimal ROI in texture analysis. Osteoarthr Cartil 28(7):941\u2013952","journal-title":"Osteoarthr Cartil"},{"key":"2403_CR9","doi-asserted-by":"crossref","unstructured":"Cheng I, Sun X, Alsufyani N, Xiong Z, Major P, Basu A (2015) Ground truth delineation for medical image segmentation based on local consistency and distribution map analysis. In: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), Milan, Italy, pp 3073\u20133076","DOI":"10.1109\/EMBC.2015.7319041"},{"key":"2403_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2019\/4253641","volume":"2019","author":"H Dhahri","year":"2019","unstructured":"Dhahri H, Al Maghayreh E, Mahmood A, Elkilani W, Nagi MF (2019) Automated breast cancer diagnosis based on machine learning algorithms. J Healthc Eng 2019:1\u201311","journal-title":"J Healthc Eng"},{"issue":"9","key":"2403_CR11","doi-asserted-by":"publisher","first-page":"1121","DOI":"10.1016\/j.humpath.2004.05.010","volume":"35","author":"J Diamond","year":"2004","unstructured":"Diamond J, Anderson NH, Bartels PH, Montironi R, Hamilton PW (2004) The use of morphological characteristics and texture analysis in the identification of tissue composition in prostatic neoplasia. Hum Pathol 35(9):1121\u20131131","journal-title":"Hum Pathol"},{"key":"2403_CR12","doi-asserted-by":"crossref","unstructured":"Doyle S, Hwang M, Shah K, Madabhushi A, Feldman M, Tomaszewski J (2007) Automated grading of prostate cancer using architectural and textural image features. In: 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, USA, pp 1284\u20131287","DOI":"10.1109\/ISBI.2007.357094"},{"key":"2403_CR13","first-page":"1","volume-title":"Introduction to Data Science and Machine Learning","author":"RJ Duggirala","year":"2019","unstructured":"Duggirala RJ (2019) Segmenting images using hybridization of k-means and fuzzy c-means algorithms. In: Sud K, Erdogmus P, Kadry S (eds) Introduction to Data Science and Machine Learning. IntechOpen, India, pp 1\u201327"},{"issue":"3","key":"2403_CR14","first-page":"1","volume":"5","author":"EE Ebrahim Ali","year":"2016","unstructured":"Ebrahim Ali EE, Feng WZ (2016) Breast cancer classification using support vector machine and neural network. Int J Sci Res 5(3):1\u20136","journal-title":"Int J Sci Res"},{"issue":"27","key":"2403_CR15","doi-asserted-by":"publisher","first-page":"3123","DOI":"10.1200\/JCO.2016.72.1209","volume":"35","author":"RA Freedman","year":"2017","unstructured":"Freedman RA, Keating NL, Pace LE, Lii J, McCarthy EP, Schonberg MA (2017) Use of surveillance mammography among older breast cancer survivors by life expectancy. J Clin Oncol 35(27):3123\u20133130","journal-title":"J Clin Oncol"},{"key":"2403_CR16","unstructured":"Gelasca E, Jiyun B, Boguslaw O, Fedorov D, Kvilekval K, Manjunath BS (2008) Evaluation and benchmark for biological image segmentation. In: IEEE International Conference on Image Processing, USA, pp 1816\u20131819"},{"key":"2403_CR17","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1016\/j.jvcir.2014.10.004","volume":"26","author":"V Gupta","year":"2015","unstructured":"Gupta V, Chaurasia V, Shandilya M (2015) Random-valued impulse noise removal using adaptive dual threshold median filter. J Vis Commun Image Represent 26:296\u2013304","journal-title":"J Vis Commun Image Represent"},{"key":"2403_CR18","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1109\/RBME.2009.2034865","volume":"2","author":"MN Gurcan","year":"2009","unstructured":"Gurcan MN, Boucheron LE, Can A, Madabhushi A, Rajpoot NM, Yener B (2009) Histopathological image analysis: a review. IEEE Rev Biomed Eng 2:147\u2013171","journal-title":"IEEE Rev Biomed Eng"},{"issue":"2","key":"2403_CR19","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/0893-6080(91)90009-T","volume":"4","author":"K Hornik","year":"1991","unstructured":"Hornik K (1991) Approximation capabilities of multilayer feedforward networks. Neural Netw 4(2):251\u2013257","journal-title":"Neural Netw"},{"issue":"20","key":"2403_CR20","doi-asserted-by":"publisher","first-page":"3660","DOI":"10.4066\/biomedicalresearch.29-18-1052","volume":"29","author":"C Kalyani","year":"2018","unstructured":"Kalyani C, Ramudu K, Reddy RG (2018) A review on optimized k-means and FCM clustering techniques for biomedical image segmentation using level set formulation. Biomed Res 29(20):3660\u20133668","journal-title":"Biomed Res"},{"issue":"1","key":"2403_CR21","doi-asserted-by":"publisher","first-page":"57","DOI":"10.3844\/ajeassp.2013.57.68","volume":"6","author":"JS Leena","year":"2013","unstructured":"Leena JS, Baskaran S, Govardhan A (2013) A robust approach to classify microcalcification in digital mammograms using contourlet transform and support vector machine. Am J Eng Appl Sci 6(1):57\u201368","journal-title":"Am J Eng Appl Sci"},{"key":"2403_CR22","doi-asserted-by":"crossref","unstructured":"Li X, Plataniotis KN (2015) Color model comparative analysis for breast cancer diagnosis using H and E stained images. In: Gurcan MN, Madabhushi A (eds) Medical Imaging 2015: Digital Pathology, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, 9420, pp 94200L-1\u201394200L-6","DOI":"10.1117\/12.2079935"},{"issue":"1","key":"2403_CR23","doi-asserted-by":"publisher","first-page":"7","DOI":"10.2217\/iim.09.9","volume":"1","author":"A Madabhushi","year":"2009","unstructured":"Madabhushi A (2009) Digital pathology image analysis: opportunities and challenges. Imaging Med 1(1):7\u201310","journal-title":"Imaging Med"},{"key":"2403_CR24","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.mcm.2008.05.010","volume":"49","author":"EA Medina","year":"2009","unstructured":"Medina EA, Paredes JIP (2009) Artificial neural network modeling techniques applied to the hydrodesulfurization process. Math Comp Model 49:207\u2013214","journal-title":"Math Comp Model"},{"issue":"8","key":"2403_CR25","doi-asserted-by":"publisher","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","volume":"42","author":"F Melgani","year":"2004","unstructured":"Melgani F, Lorenzo B (2004) Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans Geosci Remote Sens 42(8):1778\u20131790","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"5","key":"2403_CR26","first-page":"309","volume":"8","author":"AK Mohanty","year":"2011","unstructured":"Mohanty AK, Swain SK, Champati PK, Lenka SK (2011) Image mining for mammogram classification by association rule using statistical and GLCM features. International Journal of Computer Science Issues 8(5):309\u2013318","journal-title":"International Journal of Computer Science Issues"},{"issue":"2","key":"2403_CR27","doi-asserted-by":"publisher","first-page":"125","DOI":"10.4132\/jptm.2019.12.31","volume":"54","author":"S Nam","year":"2020","unstructured":"Nam S, Chong Y, Jung CK, Kwak T, Lee JY, Park J, Rho MJ, Go H (2020) Introduction to digital pathology and computer-aided pathology. J Pathol Transl Med 54(2):125\u2013134","journal-title":"J Pathol Transl Med"},{"key":"2403_CR28","doi-asserted-by":"publisher","first-page":"114622","DOI":"10.1016\/j.eswa.2021.114622","volume":"172","author":"I Khan","year":"2021","unstructured":"Khan I, Luo Z, Shaikh AK, Hedjam R (2021) Ensemble clustering using extended fuzzy k-means for cancer data analysis. Expert Syst Appl 172:114622","journal-title":"Expert Syst Appl"},{"issue":"1","key":"2403_CR29","first-page":"83","volume":"10","author":"NY Moteghaed","year":"2020","unstructured":"Moteghaed NY, Tabatabaeefar M, Mostaar A (2020) Biomedical image denoising based on hybrid optimization algorithm and sequential filters. J Biomed Phys Eng 10(1):83\u201392","journal-title":"J Biomed Phys Eng"},{"issue":"7","key":"2403_CR30","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","volume":"24","author":"T Ojala","year":"2002","unstructured":"Ojala T, Pietik\u00e4inen M, M\u00e4enp\u00e4\u00e4 T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971\u2013987","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"11","key":"2403_CR31","doi-asserted-by":"publisher","first-page":"1684","DOI":"10.1016\/j.patrec.2008.04.013","volume":"29","author":"N Orlov","year":"2008","unstructured":"Orlov N, Shamir L, Macura T, Johnston J, Eckley DM, Goldberg IG (2008) WND-CHARM: Multi-purpose image classification using compound image transforms. Pattern Recognit Lett 29(11):1684\u20131693","journal-title":"Pattern Recognit Lett"},{"issue":"4","key":"2403_CR32","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/BF03178082","volume":"11","author":"ED Pisano","year":"1998","unstructured":"Pisano ED, Zong S, Hemminger BM, DeLuca M, Johnston RE, Muller K, Braeuning MP, Pizer SM (1998) Contrast limited adaptive histogram equalization image processing to improve the detection of simulated speculations in dense mammograms. J Digit Imaging 11(4):193\u2013200","journal-title":"J Digit Imaging"},{"issue":"1\u20132","key":"2403_CR33","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/s10617-017-9200-1","volume":"22","author":"S Shanmugapriya","year":"2018","unstructured":"Shanmugapriya S, Valarmathi A (2018) Efficient fuzzy c-means based multilevel image segmentation for brain tumor detection in MR images. Des Autom Embed Syst 22(1\u20132):81\u201393","journal-title":"Des Autom Embed Syst"},{"issue":"3","key":"2403_CR34","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1080\/00051144.2020.1785784","volume":"61","author":"L Qing","year":"2020","unstructured":"Qing L, Zhigang L, Shenghui Y, Kun J, Navid R (2020) Computer-aided breast cancer diagnosis based on image segmentation and interval analysis. Automatika 61(3):496\u2013506","journal-title":"Automatika"},{"issue":"1","key":"2403_CR35","doi-asserted-by":"publisher","first-page":"e0210236","DOI":"10.1371\/journal.pone.0210236","volume":"14","author":"MZ Rodriguez","year":"2019","unstructured":"Rodriguez MZ, Comin CH, Casanova D, Bruno OM, Amancio DR, Costa LdF, Rodrigues FA (2019) Clustering algorithms: a comparative approach. PLoS One 14(1):e0210236","journal-title":"PLoS One"},{"key":"2403_CR36","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1111\/j.1749-6632.2002.tb04890.x","volume":"980","author":"AC Roque","year":"2002","unstructured":"Roque AC, Andre TC (2002) Mammography and computerized decision systems: a review. Ann N Y Acad Sci 980:83\u201394","journal-title":"Ann N Y Acad Sci"},{"key":"2403_CR37","doi-asserted-by":"crossref","unstructured":"Shan P (2018) Image segmentation method based on K-mean algorithm. EURASIP J Image Video Proc 81(2018):1\u20139","DOI":"10.1186\/s13640-018-0322-6"},{"key":"2403_CR38","doi-asserted-by":"crossref","unstructured":"Song Y, Li Q, Huang H, Feng D, Chen M, Cai W (2016) Histopathology image categorization with discriminative dimension reduction of fisher vectors. In: Hua G, J\u00e9gou H (eds) ECCV Workshops, Part I, Lecture Notes in Computer Science 9913, pp 306\u2013317","DOI":"10.1007\/978-3-319-46604-0_22"},{"key":"2403_CR39","doi-asserted-by":"crossref","unstructured":"Spanhol FA, Oliveira LS, Petitjean C, Heutte L (2016) A dataset for breast cancer histopathological image classification. IEEE Trans Biomed Eng 63(7): 1455\u20131462. https:\/\/web.inf.ufpr.br\/vri\/databases\/breast-cancer-histopathological-database-breakhis\/","DOI":"10.1109\/TBME.2015.2496264"},{"key":"2403_CR40","doi-asserted-by":"crossref","unstructured":"Spanhol FA, Oliveira LS, Petitjean C, Heutte L (2016) Breast cancer histopathological image classification using convolutional neural networks. In: International Joint Conference on Neural Networks (IJCNN), Vancouver, pp. 2560\u20132567","DOI":"10.1109\/IJCNN.2016.7727519"},{"issue":"1","key":"2403_CR41","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/s10549-019-05281-1","volume":"177","author":"R Turkki","year":"2019","unstructured":"Turkki R, Byckhov D, Lundin M et al (2019) Breast cancer outcome prediction with tumour tissue images and machine learning. Breast Cancer Res Treat 177(1):41\u201352","journal-title":"Breast Cancer Res Treat"},{"issue":"3","key":"2403_CR42","first-page":"318","volume":"1","author":"G Zorluoglu","year":"2015","unstructured":"Zorluoglu G, Agaoglu M (2015) Diagnosis of breast cancer using ensemble of data mining classification methods. Int J Bioinform Biomed Eng 1(3):318\u2013322","journal-title":"Int J Bioinform Biomed Eng"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-021-02403-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-021-02403-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-021-02403-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T17:26:38Z","timestamp":1629739598000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-021-02403-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,24]]},"references-count":42,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2021,9]]}},"alternative-id":["2403"],"URL":"https:\/\/doi.org\/10.1007\/s11517-021-02403-0","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,24]]},"assertion":[{"value":"17 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 June 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}