{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T18:48:02Z","timestamp":1772650082307,"version":"3.50.1"},"reference-count":68,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2021,3,22]],"date-time":"2021-03-22T00:00:00Z","timestamp":1616371200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2021,11,17]]},"abstract":"<jats:p>In the last decade, there have been extensive reports of world health organization (WHO) on breast cancer. About 2.1 million women are affected every year and it is the second most leading cause of cancer death in women. Initial detection and diagnosis of cancer appreciably increase the chance of saving lives and reduce treatment costs. In this paper, we perform a survey of the techniques utilized in breast cancer detection and diagnosis in image processing, machine learning (ML), and deep learning (DL). We also proposed a novel computer-vision based cost-effective method for breast cancer detection and diagnosis. Along with the detection and diagnosis of breast cancer, our proposed method is capable of finding the exact position of the abnormality present in the breast that will help in breast-conserving surgery or partial mastectomy. The proposed method is the simplest and cost-effective approach that has produced highly accurate and useful outcomes when compared with the existing approach.<\/jats:p>","DOI":"10.3233\/jifs-189848","type":"journal-article","created":{"date-parts":[[2021,3,23]],"date-time":"2021-03-23T13:39:04Z","timestamp":1616506744000},"page":"5253-5263","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":28,"title":["A cost-effective computer-vision based breast cancer diagnosis"],"prefix":"10.1177","volume":"41","author":[{"given":"Prabira Kumar","family":"Sethy","sequence":"first","affiliation":[{"name":"Department of Electronics, Sambalpur University, Odisha, India"}]},{"given":"Chanki","family":"Pandey","sequence":"additional","affiliation":[{"name":"Department of ET&amp;T Engineering, Government Engineering College, Jagdalpur, CG, India"}]},{"given":"Mohammad Rafique","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of ET&amp;T Engineering, Government Engineering College, Jagdalpur, CG, India"}]},{"given":"Santi Kumari","family":"Behera","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, VSSUT, Odisha, India"}]},{"given":"K.","family":"Vijaykumar","sequence":"additional","affiliation":[{"name":"Department of Computer Science &amp; Engineering, St. Joseph\u2019s Institute of Technology, India"}]},{"given":"Sibarama","family":"Panigrahi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, SUIIT, Odisha, India"}]}],"member":"179","published-online":{"date-parts":[[2021,3,22]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","unstructured":"ShrivastavaN. and BhartiJ. Breast tumor detection and classification based on density Multimedia Tools and Applications (2020) 1\u201321. https:\/\/doi.org\/10.1007\/s11042-020-09220-x","DOI":"10.1007\/s11042-020-09220-x"},{"key":"e_1_3_1_3_2","doi-asserted-by":"crossref","unstructured":"FeiZ. LuoZ. LiuZ. XuH. ChenL. and JiC. Analysis of the role of breast dynamic nuclear magnetic resonance imaging in the treatment of breast tumors Multimedia Tools and Applications (2019) 1\u201316. https:\/\/doi.org\/10.1007\/s11042-019-07913-6","DOI":"10.1007\/s11042-019-07913-6"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10765-012-1328-4"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-018-6089-z"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-018-6970-9"},{"key":"e_1_3_1_7_2","doi-asserted-by":"crossref","unstructured":"ShenL. MargoliesL.R. RothsteinJ.H. FluderE. and McbrideR. Deep Learning to Improve Breast Cancer Detection on Screening Mammography (2019) 1\u201312. https:\/\/doi.org\/10.1038\/s41598-019-48995-4","DOI":"10.1038\/s41598-019-48995-4"},{"key":"e_1_3_1_8_2","doi-asserted-by":"crossref","unstructured":"S.S. A A.A. M W.A. B M.L. Z J.H. S S.W. Deep learning to distinguish recalled but benign mammography images in breast cancer screening Clinical Cancer Research 2018. https:\/\/doi.org\/10.1158\/1078-0432.CCR-18-1115","DOI":"10.1158\/1078-0432.CCR-18-1115"},{"key":"e_1_3_1_9_2","doi-asserted-by":"crossref","unstructured":"Q. C J. L and X. W. Deep learning mammography model from public dataset to clinical practice-performance discrepancy examined Medical Physics 2018. https:\/\/doi.org\/10.1002\/mp.12938","DOI":"10.1002\/mp.12938"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-019-7525-4"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultras.2016.08.004"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-019-7549-9"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-019-7468-9"},{"key":"e_1_3_1_14_2","doi-asserted-by":"crossref","unstructured":"KritiV.J. and AgarwalR. Deep feature extraction and classification of breast ultrasound images Multimedia Tools and Applications (2020) 1\u201336. https:\/\/doi.org\/10.1007\/s11042-020-09337-z","DOI":"10.1007\/s11042-020-09337-z"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-018-5934-4"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2015.10.015"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2015.2458702"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-019-7570-z"},{"key":"e_1_3_1_19_2","first-page":"632","article-title":"A novel feature selection method based on an integrated data envelopment analysis and entropy model","volume":"31","author":"Bamakan S.M.H.","year":"2014","unstructured":"BamakanS.M.H. and GholamiP., A novel feature selection method based on an integrated data envelopment analysis and entropy model, Elsevier B.V.31 (2014), 632\u2013638. https:\/\/doi.org\/10.1016\/j.procs.2014.05.310","journal-title":"Elsevier B.V."},{"key":"e_1_3_1_20_2","doi-asserted-by":"crossref","unstructured":"Naga RamaDeviG. Usha RaniK. and Lavanya D. Ensemble-based hybrid approach for breast cancer data. In: Kumar A. Mozar S. (eds) ICCCE 2018. ICCCE 2018. Lecture Notes in Electrical Engineering Springer Singapore vol. 500 Springer Verlag; 2019 p. 713\u201320. https:\/\/doi.org\/10.1007\/978-981-13-0212-1_72","DOI":"10.1007\/978-981-13-0212-1_72"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2005.05.008"},{"key":"e_1_3_1_22_2","unstructured":"SalamaG.I. AbdelhalimM.B. and ZeidM.A. Breast Cancer Diagnosis on Three Different Datasets Using Multi- Classifiers 2012."},{"key":"e_1_3_1_23_2","first-page":"2","article-title":"Biological control of mycotoxins by probiotic lactic acid bacteria","volume":"2015","author":"Patel A.R.","year":"2017","unstructured":"PatelA.R., PatraF., ShahN.P. and ShuklaD., Biological control of mycotoxins by probiotic lactic acid bacteria, Dynamism in Dairy Industry and Consumer Demands2015 (2017), 2\u20134. https:\/\/doi.org\/10.1155\/2015","journal-title":"Dynamism in Dairy Industry and Consumer Demands"},{"key":"e_1_3_1_24_2","first-page":"684","article-title":"Fuzzy rough neural network and its application to feature selection,pp","volume":"2011","author":"Zhao J.Y.","year":"2011","unstructured":"ZhaoJ.Y. and ZhangZ.L., Fuzzy rough neural network and its application to feature selection,pp, Proceedings of 4th International Workshop on Advanced Computational Intelligence, IWACI2011 (2011), 684\u2013687. https:\/\/doi.org\/10.1109\/IWACI.2011.6160094","journal-title":"Proceedings of 4th International Workshop on Advanced Computational Intelligence, IWACI"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2018.07.060"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-018-6259-z"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13534-014-0148-9"},{"key":"e_1_3_1_28_2","doi-asserted-by":"crossref","unstructured":"KanojiaM.G. and AbrahamS. Breast cancer detection using RBF neural network Proceedings of the 2016 2nd International Conference on Contemporary Computing and Informatics IC3I 2016 Institute of Electrical and Electronics Engineers Inc.; (2016) pp. 363\u2013368. https:\/\/doi.org\/10.1109\/IC3I.2016.7917990","DOI":"10.1109\/IC3I.2016.7917990"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-016-3605-x"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2014.12.025"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2013.08.044"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-015-1411-7"},{"key":"e_1_3_1_33_2","doi-asserted-by":"crossref","unstructured":"AbedB.M. ShakerK. JalabH.A. ShakerH. MansoorA.M. AlwanA.F. et al. A hybrid classification algorithm approach for breast cancer diagnosis IEACon 2016 - 2016 IEEE Industrial Electronics and Applications Conference 2017. https:\/\/doi.org\/10.1109\/IEACON.2016.8067390","DOI":"10.1109\/IEACON.2016.8067390"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2015.10.005"},{"key":"e_1_3_1_35_2","doi-asserted-by":"crossref","unstructured":"BehravanH. HartikainenJ.M. Tengstr\u00f6mM. and KosmaV.M. Predicting breast cancer risk using interacting genetic and demographic factors and machine learning (2020) 1\u201316. https:\/\/doi.org\/10.1038\/s41598-020-66907-9","DOI":"10.1038\/s41598-020-66907-9"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2018.10.014"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-018-6267-z"},{"key":"e_1_3_1_38_2","doi-asserted-by":"crossref","unstructured":"PanQ. ZhangY. ChenD. and XuG. Character-based convolutional grid neural network for breast cancer classification Proceedings - 2017 International Conference on Green Informatics ICGI 2017 Institute of Electrical and Electronics Engineers Inc. (2017) pp. 41\u201348. https:\/\/doi.org\/10.1109\/ICGI.2017.31","DOI":"10.1109\/ICGI.2017.31"},{"key":"e_1_3_1_39_2","doi-asserted-by":"crossref","unstructured":"ChenD. HuangM. and LiW. Knowledge-Powered Deep Breast Tumor Classification with Multiple Medical Reports IEEE\/ACM Transactions on Computational Biology and Bioinformatics (2019) 1\u20131. https:\/\/doi.org\/10.1109\/tcbb.2019.2955484","DOI":"10.1109\/TCBB.2019.2955484"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2019.03.022"},{"key":"e_1_3_1_41_2","doi-asserted-by":"crossref","unstructured":"XiaoT. LiuL. LiK. QinW. YuS. and LiZ. Comparison of Transferred Deep Neural Networks in Ultrasonic Breast Masses Discrimination BioMed Research International (2018) 2018. https:\/\/doi.org\/10.1155\/2018\/4605191","DOI":"10.1155\/2018\/4605191"},{"key":"e_1_3_1_42_2","doi-asserted-by":"crossref","unstructured":"DeYu S. LiuL.L. WangZ.Y. DaiG.Z. and XieY.Q. Transferring deep neural networks for the differentiation of mammographic breast lesions Science China Technological Sciences 2019. https:\/\/doi.org\/10.1007\/s11431-017-9317-3","DOI":"10.1007\/s11431-017-9317-3"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-019-08248-y"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-018-6026-1"},{"key":"e_1_3_1_45_2","doi-asserted-by":"crossref","unstructured":"OleszkiewiczW. CichoszP. Jagodzi\u0144skiD. MatysiewiczM. Neumann\u0141. NowakR.M. et al. Application of SVM classifier in thermographic image classification for early detection of breast cancer Photonics Applications in Astronomy Communications Industry and High-Energy Physics Experiments (2016) 2016. https:\/\/doi.org\/10.1117\/12.2249063","DOI":"10.1117\/12.2249063"},{"key":"e_1_3_1_46_2","doi-asserted-by":"crossref","unstructured":"Anji ReddyV. and SoniB. Breast Cancer Identification and Diagnosis Techniques. In: Rout J. Rout M. Das H. (eds) Machine Learning for Intelligent Decision Science. Algorithms for Intelligent Systems. Springer Singapore Springer Singapore; 2020 p. 49\u201370. https:\/\/doi.org\/10.1007\/978-981-15-3689-2_3","DOI":"10.1007\/978-981-15-3689-2_3"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-019-07917-2"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-018-6390-x"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2016.07.020"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2891622"},{"key":"e_1_3_1_51_2","doi-asserted-by":"crossref","unstructured":"VakaA.R. SoniB. and K. SR Breast cancer detection by leveraging Machine Learning ICT Express 2020. https:\/\/doi.org\/10.1016\/j.icte.2020.04.009","DOI":"10.1016\/j.icte.2020.04.009"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-018-6560-x"},{"key":"e_1_3_1_53_2","doi-asserted-by":"crossref","unstructured":"TingF.F. TanY.J. and SimK.S. Convolutional neural network improvement for breast cancer classification Expert Systems with Applications 2019. https:\/\/doi.org\/10.1016\/j.eswa.2018.11.008","DOI":"10.1016\/j.eswa.2018.11.008"},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2018.01.011"},{"key":"e_1_3_1_55_2","unstructured":"TangC-M. CuiX-M. XiangY.U. and FanY. Five Classifications of Mammography Images Based on Deep Cooperation Convolutional Neural Network Technology and Sciences (ASRJETS) American Scientific Research Journal for Engineering 2019."},{"key":"e_1_3_1_56_2","doi-asserted-by":"crossref","unstructured":"FujiokaT. KatsutaL. KubotaK. MoriM. KikuchiY. KatoA. et al. classification of breast masses on ultrasound shear wave elastography using convolutional neural networks Ultrasonic Imaging 2020. https:\/\/doi.org\/10.1177\/0161734620932609","DOI":"10.1177\/0161734620932609"},{"key":"e_1_3_1_57_2","doi-asserted-by":"crossref","unstructured":"PerreA.C. AlexandreL.A. and FreireL.C. Lesion classification in mammograms using convolutional neural networks and transfer learning Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization 2019. https:\/\/doi.org\/10.1080\/21681163.2018.1498392","DOI":"10.1080\/21681163.2018.1498392"},{"key":"e_1_3_1_58_2","doi-asserted-by":"crossref","unstructured":"F. G T. W J. L B. Z L. R D. S et al. SD-CNN: A shallow-deep CNN for improved breast cancer diagnosis Computerized Medical Imaging and Graphics 2018. https:\/\/doi.org\/10.1016\/j.compmedimag.2018.09.004","DOI":"10.1016\/j.compmedimag.2018.09.004"},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-019-7479-6"},{"key":"e_1_3_1_60_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-019-7419-5"},{"key":"e_1_3_1_61_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-020-08692-1"},{"key":"e_1_3_1_62_2","unstructured":"ParkerJ. RickettsI. SavageJ. and StamatakisE. MAMMOGRAPHIC IMAGE ANALYSIS SOCIETY Mammographic Database 2015."},{"key":"e_1_3_1_63_2","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/457906"},{"key":"e_1_3_1_64_2","doi-asserted-by":"crossref","unstructured":"KadamV.J. JadhavS.M. and VijayakumarK. Breast Cancer Diagnosis Using Feature Ensemble Learning Based on Stacked Sparse Autoencoders and Softmax Regression Journal of Medical Systems (2019) 43. https:\/\/doi.org\/10.1007\/s10916-019-1397-z","DOI":"10.1007\/s10916-019-1397-z"},{"key":"e_1_3_1_65_2","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging4010014"},{"key":"e_1_3_1_66_2","doi-asserted-by":"crossref","unstructured":"Id GFS Id GRH Id BJN and DengJ. Predicting breast cancer risk using personal health data and machine learning models (2019) 1\u201317. https:\/\/doi.org\/10.1371\/journal.pone.0226765","DOI":"10.1371\/journal.pone.0226765"},{"key":"e_1_3_1_67_2","doi-asserted-by":"crossref","unstructured":"AdelM. KotbA. FaragO. DarweeshM.S. and MostafaH. Breast Cancer Diagnosis Using Image Processing and Machine Learning for Elastography Images 2019 8th International Conference on Modern Circuits and Systems Technologies (MOCAST) IEEE (2019) pp. 1\u20134. https:\/\/doi.org\/10.1109\/MOCAST.2019.8741846","DOI":"10.1109\/MOCAST.2019.8741846"},{"key":"e_1_3_1_68_2","first-page":"254","article-title":"Breast cancer detection using convolutional neural networks","volume":"027","author":"Hasan M.","year":"2019","unstructured":"HasanM., DasBarman S., IslamS. and RezaA.W., Breast cancer detection using convolutional neural networks, ACM International Conference Proceeding Series027 (2019), 254\u2013158. https:\/\/doi.org\/10.1145\/3330482.3330525","journal-title":"ACM International Conference Proceeding Series"},{"key":"e_1_3_1_69_2","doi-asserted-by":"crossref","unstructured":"SadhukhanS. UpadhyayN. and ChakrabortyP. Breast Cancer Diagnosis Using Image Processing and Machine Learning Advances in Intelligent Systems and Computing vol. 937 Springer Verlag; (2020) pp. 113\u201327. https:\/\/doi.org\/10.1007\/978-981-13-7403-6_12","DOI":"10.1007\/978-981-13-7403-6_12"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-189848","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-189848","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-189848","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T00:53:42Z","timestamp":1769993622000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-189848"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,22]]},"references-count":68,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,11,17]]}},"alternative-id":["10.3233\/JIFS-189848"],"URL":"https:\/\/doi.org\/10.3233\/jifs-189848","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,22]]}}}