{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T18:05:01Z","timestamp":1760551501652,"version":"3.37.3"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"21-23","license":[{"start":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T00:00:00Z","timestamp":1626393600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T00:00:00Z","timestamp":1626393600000},"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":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,9]]},"DOI":"10.1007\/s11042-021-11199-y","type":"journal-article","created":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T01:02:31Z","timestamp":1626397351000},"page":"31647-31670","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["BrC-MCDLM: breast Cancer detection using Multi-Channel deep learning model"],"prefix":"10.1007","volume":"80","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1389-3456","authenticated-orcid":false,"given":"Jitendra V.","family":"Tembhurne","sequence":"first","affiliation":[]},{"given":"Anupama","family":"Hazarika","sequence":"additional","affiliation":[]},{"given":"Tausif","family":"Diwan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,16]]},"reference":[{"issue":"4","key":"11199_CR1","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1515\/jaiscr-2015-0031","volume":"5","author":"MH Aghdam","year":"2015","unstructured":"Aghdam MH, Heidari S (2015) Feature selection using particle swarm optimization in text categorization. J Artificial Intell Soft Comput Res 5(4):231\u2013238","journal-title":"J Artificial Intell Soft Comput Res"},{"issue":"2","key":"11199_CR2","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1111\/his.12387","volume":"65","author":"KH Allison","year":"2014","unstructured":"Allison KH, Reisch LM, Carney PA, Weaver DL, Schnitt SJ, O ' Malley FP, Elmore JG (2014) Understanding diagnostic variability in breast pathology: lessons learned from an expert consensus review panel. Histopathology 65(2):240\u2013251. https:\/\/doi.org\/10.1111\/his.12387","journal-title":"Histopathology"},{"issue":"4","key":"11199_CR3","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1007\/s10278-019-00182-7","volume":"32","author":"MZ Alom","year":"2019","unstructured":"Alom MZ, Yakopcic C, Taha TM, Asari VK (2019) Breast cancer classification from histopathological images with inception recurrent residual convolutional neural network. J Digit Imaging 32(4):605\u2013617","journal-title":"J Digit Imaging"},{"issue":"3","key":"11199_CR4","doi-asserted-by":"publisher","first-page":"445","DOI":"10.3390\/electronics9030445","volume":"9","author":"L Alzubaidi","year":"2020","unstructured":"Alzubaidi L, Al-Shamma O, Fadhel MA, Farhan L, Zhang J, Dyan Y (2020) Optimizing the performance of breast Cancer classification by employing the same domain transfer learning from hybrid deep convolutional neural network model. Electronics 9(3):445. https:\/\/doi.org\/10.3390\/electronics9030445","journal-title":"Electronics"},{"key":"11199_CR5","unstructured":"American Cancer Society (2020) Breast Cancer Facts and Figures 2019\u20132020. https:\/\/www.cancer.org\/content\/dam\/cancer-org\/research\/cancer-facts-and-statistics\/breast-cancer-facts-and-figures\/breastcancer-facts-and-figures-2019-2020.pdf."},{"key":"11199_CR6","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1007\/978-3-319-93000-8_89","volume-title":"International conference image analysis and recognition","author":"R Awan","year":"2018","unstructured":"Awan R, Koohbanani N, Shaban M, Lisowska A, Rajpoot N (2018) Context-aware learning using transferable features for classification of breast cancer histology images. In: International conference image analysis and recognition. Springer, Cham, pp 788\u2013795"},{"issue":"6","key":"11199_CR7","doi-asserted-by":"publisher","first-page":"394","DOI":"10.3322\/caac.21492","volume":"68","author":"F Bray","year":"2018","unstructured":"Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global Cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer J Clin 68(6):394\u2013424. https:\/\/doi.org\/10.3322\/caac.21492","journal-title":"CA: A Cancer J Clin"},{"key":"11199_CR8","unstructured":"Chollet F (2015) Keras. https:\/\/github.com\/fchollet\/keras."},{"key":"11199_CR9","unstructured":"Cytecare Cancer Hopitals (2020) Statistics of breast cancer in India. https:\/\/cytecare.com\/blog\/statistics-ofbreast-cancer\/. Accessed 10 June, 2020."},{"key":"11199_CR10","doi-asserted-by":"publisher","first-page":"763","DOI":"10.1007\/978-3-319-93000-8-86","volume-title":"International conference image analysis and recognition","author":"C Ferreira","year":"2018","unstructured":"Ferreira C, Melo T, Sousa P, Meyer M, Shakibapour E, Costa P, Campilho A (2018) Classification of breast cancer histology images through transfer learning using a pre-trained inception ResNet v2. In: International conference image analysis and recognition. Springer, Cham, pp 763\u2013770. https:\/\/doi.org\/10.1007\/978-3-319-93000-8-86"},{"issue":"3","key":"11199_CR11","doi-asserted-by":"publisher","first-page":"949","DOI":"10.1109\/JSYST.2013.2279415","volume":"8","author":"YM George","year":"2014","unstructured":"George YM, Zayed HH, Roushdy MI, Elbagoury BM (2014) Remote computer-aided breast Cancer detection and diagnosis system based on cytological images. IEEE Syst J 8(3):949\u2013964. https:\/\/doi.org\/10.1109\/JSYST.2013.2279415","journal-title":"IEEE Syst J"},{"key":"11199_CR12","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1007\/978-3-319-93000-8_94","volume-title":"International conference image analysis and recognition","author":"Y Guo","year":"2018","unstructured":"Guo Y, Dng H, Song F, Zhu C, Liu J (2018) Breast Cancer histology image classification based on deep neural networks. In: International conference image analysis and recognition. Springer, Cham, pp 827\u2013836"},{"key":"11199_CR13","doi-asserted-by":"publisher","unstructured":"Gupta V, Bhavsar A (2017) Breast cancer histopathological image classification: is magnification important?. In: proceedings of the IEEE conference on computer vision and pattern recognition workshops - CVPRW\u201917, pp 17-24. doi: https:\/\/doi.org\/10.1109\/CVPRW.2017.107.","DOI":"10.1109\/CVPRW.2017.107"},{"key":"11199_CR14","doi-asserted-by":"publisher","unstructured":"Kassani SH, Kassani PH, Wesolowski MJ, Schneider KA, Deters R (2019) Breast cancer diagnosis with transfer learning and global pooling. In: Proceedings of the international conference on information and communication technology convergence \u2013ICTC\u201919, pp 519\u2013524. https:\/\/doi.org\/10.1109\/ICTC46691.2019.8939878","DOI":"10.1109\/ICTC46691.2019.8939878"},{"key":"11199_CR15","doi-asserted-by":"crossref","unstructured":"Malon CD, Cosatto E (2013) Classification of mitotic figures with convolutional neural networks and seeded blob features Journal of Pathology Informatics 4.","DOI":"10.4103\/2153-3539.112694"},{"key":"11199_CR16","unstructured":"Mayo Clinic (2019) Breast Cancer. https:\/\/www.mayoclinic.org\/diseases-conditions\/breastcancer\/diagnosis-treatment\/drc-20352475. Accessed 19 July 2020."},{"issue":"4","key":"11199_CR17","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.1590\/1413-81232015204.17772014.","volume":"20","author":"A Migowski","year":"2015","unstructured":"Migowski A (2015) Early detection of breast cancer and the interpretation of results of survival studies. Ciencia & saude coletiva 20(4):1309\u20131310. https:\/\/doi.org\/10.1590\/1413-81232015204.17772014. (Portugese)","journal-title":"Ciencia & saude coletiva"},{"issue":"4","key":"11199_CR18","doi-asserted-by":"publisher","first-page":"729","DOI":"10.3233\/THC-181277","volume":"26","author":"M Milosevic","year":"2018","unstructured":"Milosevic M, Jankovic D, Milenkovic A, Stojanov D (2018) Early diagnosis and detection of breast cancer. Technol Health Care 26(4):729\u2013759. https:\/\/doi.org\/10.3233\/THC-181277","journal-title":"Technol Health Care"},{"key":"11199_CR19","doi-asserted-by":"publisher","first-page":"18447","DOI":"10.1007\/s11042-020-08692-1","volume":"79","author":"G Murtaza","year":"2020","unstructured":"Murtaza G, Shuib L, Wahab AWA, Mujtaba G, Raza G (2020) Ensembled deep convolution neural network-based breast cancer classification with misclassification reduction algorithms. Multimed Tools Appl 79:18447\u201318479. https:\/\/doi.org\/10.1007\/s11042-020-08692-1","journal-title":"Multimed Tools Appl"},{"key":"11199_CR20","doi-asserted-by":"crossref","unstructured":"Nahid AA, Mehrabi MA, Kong Y (2018) Histopathological breast cancer image classification by deep neural network techniques guided by local clustering, BioMed Research International","DOI":"10.1155\/2018\/2362108"},{"key":"11199_CR21","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1007\/978-3-319-93000-8_99","volume-title":"International conference image analysis and recognition. In: international conference image analysis and recognition","author":"W Nawaz","year":"2018","unstructured":"Nawaz W, Ahmed S, Tahir A, Khan HA (2018) Classification of breast cancer histology images using AlexNet. In: International conference image analysis and recognition. In: international conference image analysis and recognition. Springer, Cham, pp 869\u2013876"},{"issue":"3","key":"11199_CR22","doi-asserted-by":"publisher","first-page":"990","DOI":"10.1016\/j.eswa.2014.09.020","volume":"42","author":"R Rouhi","year":"2015","unstructured":"Rouhi R, Jafari M, Kasaei M, Keshavarzian P (2015) Benign and malignant breast tumors classification based on region growing and CNN segmentation. Expert Syst Appl 42(3):990\u20131002","journal-title":"Expert Syst Appl"},{"issue":"9","key":"11199_CR23","doi-asserted-by":"publisher","first-page":"095005","DOI":"10.1088\/1361-6560\/aabb5b","volume":"63","author":"RK Samala","year":"2018","unstructured":"Samala RK, Chan HP, Hadjiiski LM, Helvie MA, Richter C, Cha K (2018) Evolutionary pruning of transfer learned deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis. Phys Med Biol 63(9):095005. https:\/\/doi.org\/10.1088\/1361-6560\/aabb5b","journal-title":"Phys Med Biol"},{"key":"11199_CR24","unstructured":"Sarker MI, Kim H, Tarasov D, Akhmetzanov D (2019) Inception architecture and residual connections in classification of breast Cancer histology images. arXiv 2019 arXiv preprint arXiv:1912.04619"},{"key":"11199_CR25","unstructured":"Sommer C, Fiaschi L, Hamprecht FA, Gerlich DW (2012) Learning-based mitotic cell detection in histopathological images. In: Proceedings of the 21st international conference on pattern recognition \u2013 ICPR\u201912, pp 2306\u20132309"},{"issue":"7","key":"11199_CR26","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 (2016) A dataset for breast cancer histopathological image classification. IEEE Trans Biomed Eng 63(7):1455\u20131462","journal-title":"IEEE Trans Biomed Eng"},{"key":"11199_CR27","unstructured":"US Cancer Statistics Working Group (2017) United States cancer statistics: 1999\u20132014 incidence and mortality web-based report. US Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute, Atlanta"},{"key":"11199_CR28","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1007\/978-3-319-93000-8_104","volume-title":"International conference image analysis and recognition","author":"YS Vang","year":"2018","unstructured":"Vang YS, Chen Z, Xie X (2018) Deep learning framework for multi-class breast Cancer histology image classification. In: International conference image analysis and recognition. Springer, Cham, pp 914\u2013922"},{"key":"11199_CR29","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1007\/978-3-319-93000-8_84","volume-title":"International conference image analysis and recognition","author":"Z Wang","year":"2018","unstructured":"Wang Z, Dong N, Dai W, Rosario SD, Xing EP (2018) Classification of breast cancer histopathological images using convolutional neural networks with hierarchical loss and global pooling. In: International conference image analysis and recognition. Springer, Cham, pp 745\u2013753"},{"key":"11199_CR30","unstructured":"WHO. Breast cancer. https:\/\/www.who.int\/cancer\/prevention\/diagnosis-screening\/breast-cancer\/en\/. Accessed 18 July 2020."},{"issue":"7","key":"11199_CR31","doi-asserted-by":"publisher","first-page":"1405","DOI":"10.1007\/s00138-012-0459-8","volume":"24","author":"L Zhang","year":"2013","unstructured":"Zhang L, Zhang B, Coenen F, Lu W (2013) Breast cancer diagnosis from biopsy images with highly reliable random subspace classifier ensembles. Mach Vis Appl 24(7):1405\u20131420. https:\/\/doi.org\/10.1007\/s00138-012-0459-8","journal-title":"Mach Vis Appl"},{"key":"11199_CR32","doi-asserted-by":"publisher","unstructured":"Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition \u2013 CVPR\u201917, pp 1800\u20131807. https:\/\/doi.org\/10.1109\/CVPR.2017.195","DOI":"10.1109\/CVPR.2017.195"},{"key":"11199_CR33","doi-asserted-by":"publisher","unstructured":"Xie S, Girshick R, Doll\u00e1r P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition \u2013 CVPR\u201917, pp 1492\u20131500. https:\/\/doi.org\/10.1109\/CVPR.2017.634","DOI":"10.1109\/CVPR.2017.634"},{"key":"11199_CR34","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-first AAAI conference on artificial intelligence \u2013 AAAI\u201917, pp 4278\u20134284. https:\/\/dl.acm.org\/doi\/10.5555\/3298023.329818838","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"11199_CR35","doi-asserted-by":"publisher","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition \u2013 CVPR\u201917, pp 4700\u20134708. https:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"11199_CR36","doi-asserted-by":"publisher","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition \u2013 CVPR\u201916, pp 2818\u20132826. https:\/\/doi.org\/10.1109\/CVPR.2016.308","DOI":"10.1109\/CVPR.2016.308"},{"key":"11199_CR37","doi-asserted-by":"publisher","first-page":"e6201","DOI":"10.7717\/peerj.6201","volume":"7","author":"DA Ragab","year":"2019","unstructured":"Ragab DA, Sharkas M, Marshall S, Ren J (2019) Breast cancer detection using deep convolutional neural networks and support vector machines. PeerJ 7:e6201","journal-title":"PeerJ"},{"key":"11199_CR38","unstructured":"Hadush S, Girmay Y, Sinamo A, Hagos G (2020) Breast Cancer detection using convolutional neural networks. arXiv preprint arXiv:2003.07911"},{"issue":"1","key":"11199_CR39","first-page":"149","volume":"26","author":"MK Kele\u015f","year":"2019","unstructured":"Kele\u015f MK (2019) Breast cancer prediction and detection using data mining classification algorithms: a comparative study. Tehni\u010dki vjesnik 26(1):149\u2013155","journal-title":"Tehni\u010dki vjesnik"},{"key":"11199_CR40","doi-asserted-by":"publisher","unstructured":"Zheng J, Lin D, Gao Z, Wang S, He M, Fan J (2020) Deep learning assisted efficient AdaBoost algorithm for breast cancer detection and early diagnosis. IEEE Access 8:96946\u201396954. https:\/\/doi.org\/10.1109\/ACCESS.2020.2993536","DOI":"10.1109\/ACCESS.2020.2993536"},{"key":"11199_CR41","doi-asserted-by":"publisher","first-page":"27779","DOI":"10.1109\/ACCESS.2020.2964276","volume":"8","author":"Y Wang","year":"2020","unstructured":"Wang Y, Lei B, Elazab A, Tan EL, Wang W, Huang F, Wang T (2020) Breast cancer image classification via multi-network features and dual-network orthogonal low-rank learning. IEEE Access 8:27779\u201327792","journal-title":"IEEE Access"},{"key":"11199_CR42","first-page":"322","volume-title":"Joint European-US workshop on applications of invariance in computer vision","author":"G Hamed","year":"2020","unstructured":"Hamed G, Marey MAER, Amin SES, Tolba MF (2020) Deep learning in breast Cancer detection and classification. In: Joint European-US workshop on applications of invariance in computer vision. Springer, Cham, pp 322\u2013333"},{"issue":"2","key":"11199_CR43","doi-asserted-by":"publisher","first-page":"111","DOI":"10.3390\/healthcare8020111","volume":"8","author":"MF Ak","year":"2020","unstructured":"Ak MF (2020) A comparative analysis of breast Cancer detection and diagnosis using data visualization and machine learning applications. Healthcare 8(2):111 Multidisciplinary digital publishing institute","journal-title":"Healthcare"},{"issue":"2","key":"11199_CR44","doi-asserted-by":"publisher","first-page":"62","DOI":"10.2478\/jdis-2020-0012","volume":"5","author":"BP Vrigazova","year":"2020","unstructured":"Vrigazova BP (2020) Detection of malignant and benign breast Cancer using the ANOVA-BOOTSTRAP-SVM. J Data Inform Sci 5(2):62\u201375","journal-title":"J Data Inform Sci"},{"issue":"12","key":"11199_CR45","doi-asserted-by":"publisher","first-page":"160558","DOI":"10.1098\/rsos.160558","volume":"3","author":"A Chan","year":"2016","unstructured":"Chan A, Tuszynski JA (2016) Automatic prediction of tumour malignancy in breast cancer with fractal dimension. R Soc Open Sci 3(12):160558","journal-title":"R Soc Open Sci"},{"key":"11199_CR46","doi-asserted-by":"publisher","first-page":"24680","DOI":"10.1109\/ACCESS.2018.2831280","volume":"6","author":"D Bardou","year":"2018","unstructured":"Bardou D, Zhang K, Ahmad SM (2018) Classification of breast Cancer based on histology images using convolutional neural networks. IEEE Access 6:24680\u201324693","journal-title":"IEEE Access"},{"issue":"1","key":"11199_CR47","first-page":"49","volume":"7","author":"MA Kahya","year":"2017","unstructured":"Kahya MA, Al-Hayani W, Algamal ZY (2017) Classification of breast cancer histopathology images based on adaptive sparse support vector machine. J Appl Mathematics Bioinform 7(1):49","journal-title":"J Appl Mathematics Bioinform"},{"issue":"4","key":"11199_CR48","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1007\/s10278-019-00182-7","volume":"32","author":"MZ Alom","year":"2019","unstructured":"Alom MZ, Yakopcic C, Nasrin MS, Taha TM, Asari VK (2019) Breast cancer classification from histopathological images with inception recurrent residual convolutional neural network. J Digit Imaging 32(4):605\u2013617","journal-title":"J Digit Imaging"},{"key":"11199_CR49","doi-asserted-by":"publisher","unstructured":"Arslan AK, Ya\u015far \u015e, \u00c7olak C (2019) Breast cancer classification using a constructed convolutional neural network on the basis of the histopathological images by an interactive web-based interface. In: Proceedings of the 3rd international symposium on multidisciplinary studies and innovative technologies \u2013 ISMSIT\u201919, pp 1\u20135. https:\/\/doi.org\/10.1109\/ISMSIT.2019.8932942","DOI":"10.1109\/ISMSIT.2019.8932942"},{"key":"11199_CR50","doi-asserted-by":"publisher","unstructured":"BBayramoglu N, Kannala J, Heikkil\u00e4 J (2016) Deep learning for magnification independent breast cancer histopathology image classification. In: Proceedings of the 23rd international conference on pattern recognition \u2013ICPR\u201916, pp 2440\u20132445. https:\/\/doi.org\/10.1109\/ICPR.2016.7900002","DOI":"10.1109\/ICPR.2016.7900002"},{"key":"11199_CR51","doi-asserted-by":"publisher","unstructured":"Spanhol FA, Oliveira LS, Cavalin PR, Petitjean C, Heutte L (2017) Deep features for breast cancer histopathological image classification. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics \u2013SMC\u201917, pp 1868\u20131873. https:\/\/doi.org\/10.1109\/SMC.2017.8122889","DOI":"10.1109\/SMC.2017.8122889"},{"issue":"6","key":"11199_CR52","doi-asserted-by":"publisher","first-page":"e0177544","DOI":"10.1371\/journal.pone.0177544","volume":"12","author":"T Ara\u00fajo","year":"2017","unstructured":"Ara\u00fajo T, Aresta G, Castro E, Rouco J, Aguiar P, Eloy C, Campilho A (2017) Classification of breast cancer histology images using convolutional neural networks. PLoS One 12(6):e0177544","journal-title":"PLoS One"},{"issue":"3","key":"11199_CR53","doi-asserted-by":"publisher","first-page":"e0214587","DOI":"10.1371\/journal.pone.0214587","volume":"14","author":"Y Jiang","year":"2019","unstructured":"Jiang Y, Chen L, Zhang H, Xiao X (2019) Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module. PLoS One 14(3):e0214587. https:\/\/doi.org\/10.1371\/journal.pone.0214587","journal-title":"PLoS One"},{"key":"11199_CR54","unstructured":"Rajarapollu PR, Mankar VR (2017) Bicubic interpolation algorithm implementation for image appearance enhancement. Int J Comput Sci Inf Technol 8(2):23\u201326 http:\/\/www.ijcst.com\/vol8\/8.2\/4-prachi-r-rajarapollu.pdf"},{"key":"11199_CR55","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-20521-8_74","volume-title":"Advances in computational intelligence, IWANN\u201919. Lecture notes in computer science","author":"G L\u00f3pez-Garc\u00eda","year":"2019","unstructured":"L\u00f3pez-Garc\u00eda G, Jerez JM, Franco L, Veredas FJ (2019) A transfer-learning approach to feature extraction from Cancer transcriptomes with deep autoencoders. In: Advances in computational intelligence, IWANN\u201919. Lecture notes in computer science, vol 11506. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-20521-8_74"},{"key":"11199_CR56","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-91008-6_58","volume-title":"advances in computer science for engineering and education, ICCSEEA\u201918. Advances in intelligent systems and computing","author":"R Tkachenko","year":"2019","unstructured":"Tkachenko R, Izonin I (2019) model and principles for the implementation of neural-like structures based on geometric data transformations. In: advances in computer science for engineering and education, ICCSEEA\u201918. Advances in intelligent systems and computing, vol 754. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-319-91008-6_58"},{"key":"11199_CR57","doi-asserted-by":"publisher","unstructured":"Wenzhong L, Huanlan L, Caijian H, Liangjun Z (2020) Classifications of breast cancer images by deep learning. medRxiv. https:\/\/doi.org\/10.1101\/2020.06.13.20130633","DOI":"10.1101\/2020.06.13.20130633"},{"key":"11199_CR58","doi-asserted-by":"publisher","first-page":"878","DOI":"10.1016\/j.procs.2020.03.427","volume":"167","author":"K Gupta","year":"2020","unstructured":"Gupta K, Chawla N (2020) Analysis of histopathological images for prediction of breast Cancer using traditional classifiers with pre-trained CNN. Procedia Comput Sci 167:878\u2013889","journal-title":"Procedia Comput Sci"},{"key":"11199_CR59","doi-asserted-by":"publisher","first-page":"6","DOI":"10.14569\/IJACSA.2018.090645","volume":"9","author":"M Nawaz","year":"2018","unstructured":"Nawaz M, Sewissy AA, Soliman THA (2018) Multi-class breast Cancer classification using deep learning convolutional neural network. Int J Advanced Comput Sci Appl (IJACSA) 9:6. https:\/\/doi.org\/10.14569\/IJACSA.2018.090645","journal-title":"Int J Advanced Comput Sci Appl (IJACSA)"},{"key":"11199_CR60","doi-asserted-by":"publisher","unstructured":"Patil A, Tamboli D, Meena S, Anand D, Sethi A (2019) Breast Cancer histopathology image classification and localization using multiple instance learning. In: Proceedings of the IEEE international WIE conference on electrical and computer engineering -WIECON-ECE\u201919, pp 1\u20134. https:\/\/doi.org\/10.1109\/WIECON-ECE48653.2019.9019916","DOI":"10.1109\/WIECON-ECE48653.2019.9019916"},{"issue":"16","key":"11199_CR61","doi-asserted-by":"publisher","first-page":"4373","DOI":"10.3390\/s20164373","volume":"20","author":"Z Hameed","year":"2020","unstructured":"Hameed Z, Zahia S, Garcia-Zapirain B, Javier Aguirre J, Mar\u00eda Vanegas A (2020) Breast cancer histopathology image classification using an ensemble of deep learning models. Sensors 20(16):4373","journal-title":"Sensors"},{"key":"11199_CR62","doi-asserted-by":"publisher","unstructured":"Ali A, Zhu Y, Zakarya M (2021) A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing. Multimed Tools Appl:1\u201333. https:\/\/doi.org\/10.1007\/s11042-020-10486-4","DOI":"10.1007\/s11042-020-10486-4"},{"key":"11199_CR63","doi-asserted-by":"publisher","unstructured":"Ali A, Zhu Y, Chen Q, Yu J, Cai H (2019) Leveraging spatio-temporal patterns for predicting citywide traffic crowd flows using deep hybrid neural networks. In: Proceedings of the IEEE 25th international conference on parallel and distributed systems \u2013 ICPADS\u201919, pp 125\u2013132. https:\/\/doi.org\/10.1109\/ICPADS47876.2019.00025","DOI":"10.1109\/ICPADS47876.2019.00025"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-11199-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-021-11199-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-11199-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,4]],"date-time":"2023-01-04T14:54:49Z","timestamp":1672844089000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-021-11199-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,16]]},"references-count":63,"journal-issue":{"issue":"21-23","published-print":{"date-parts":[[2021,9]]}},"alternative-id":["11199"],"URL":"https:\/\/doi.org\/10.1007\/s11042-021-11199-y","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2021,7,16]]},"assertion":[{"value":"9 September 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 May 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 July 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}