{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T11:21:29Z","timestamp":1785410489339,"version":"3.56.0"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T00:00:00Z","timestamp":1700438400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T00:00:00Z","timestamp":1700438400000},"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":[[2024,2]]},"DOI":"10.1007\/s00521-023-09165-w","type":"journal-article","created":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T14:01:45Z","timestamp":1700488905000},"page":"2101-2117","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Breast lesion classification from mammograms using deep neural network and test-time augmentation"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9637-6157","authenticated-orcid":false,"given":"Parita","family":"Oza","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paawan","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samir","family":"Patel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,11,20]]},"reference":[{"key":"9165_CR1","unstructured":"Cancer Facts and Figures 2021. https:\/\/www.cancer.org\/research\/cancer-facts-statistics\/all-cancer-facts-figures\/cancer-facts-figures-2021.html. Accessed 31 Mar 2022 (2021)"},{"issue":"3","key":"9165_CR2","doi-asserted-by":"publisher","first-page":"209","DOI":"10.5306\/wjco.v13.i3.209","volume":"13","author":"R Mehrotra","year":"2022","unstructured":"Mehrotra R, Yadav K (2022) Breast cancer in india: present scenario and the challenges ahead. World J Clin Oncol 13(3):209","journal-title":"World J Clin Oncol"},{"key":"9165_CR3","unstructured":"Alarming facts about breast cancer in India. https:\/\/www.oncostem.com\/blog\/alarming-facts-about-breast-cancer-in-india. Accessed 26 Mar 2022 (2019)"},{"issue":"6","key":"9165_CR4","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 Cancer J Clin 68(6):394\u2013424","journal-title":"CA Cancer J Clin"},{"key":"9165_CR5","doi-asserted-by":"crossref","unstructured":"Oza P, Sharma P, Patel S (2021) Machine learning applications for computer-aided medical diagnostics. In: Proceedings of second international conference on computing, communications, and cyber-security, pp 377\u2013392 (2021). Springer","DOI":"10.1007\/978-981-16-0733-2_26"},{"key":"9165_CR6","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/978-981-16-8248-3_19","volume":"1","author":"P Oza","year":"2022","unstructured":"Oza P, Sharma P, Patel S (2022) A drive through computer-aided diagnosis of breast cancer: a comprehensive study of clinical and technical aspects. Recent Innov Comput 1:233\u2013249","journal-title":"Recent Innov Comput"},{"key":"9165_CR7","doi-asserted-by":"crossref","unstructured":"Pillai R, Oza P, Sharma P (2020) Review of machine learning techniques in health care. In: Proceedings of ICRIC 2019, pp 103\u2013111. Springer","DOI":"10.1007\/978-3-030-29407-6_9"},{"issue":"3","key":"9165_CR8","doi-asserted-by":"publisher","first-page":"37","DOI":"10.3390\/jimaging5030037","volume":"5","author":"L Tsochatzidis","year":"2019","unstructured":"Tsochatzidis L, Costaridou L, Pratikakis I (2019) Deep learning for breast cancer diagnosis from mammograms-a comparative study. J Imaging 5(3):37","journal-title":"J Imaging"},{"key":"9165_CR9","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25 (2012)"},{"key":"9165_CR10","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"9165_CR11","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"9165_CR12","unstructured":"Tan M, Le Q (2019) Efficientnet: rethinking model scaling for convolutional neural networks. In: International conference on machine learning, pp 6105\u20136114. PMLR"},{"key":"9165_CR13","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"9165_CR14","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging7090190","author":"P Oza","year":"2021","unstructured":"Oza P, Sharma P, Patel S, Bruno A (2021) A bottom-up review of image analysis methods for suspicious region detection in mammograms. J Imaging. https:\/\/doi.org\/10.3390\/jimaging7090190","journal-title":"J Imaging"},{"key":"9165_CR15","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.neucom.2019.01.103","volume":"338","author":"G Wang","year":"2019","unstructured":"Wang G, Li W, Aertsen M, Deprest J, Ourselin S, Vercauteren T (2019) Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks. Neurocomputing 338:34\u201345","journal-title":"Neurocomputing"},{"key":"9165_CR16","doi-asserted-by":"publisher","first-page":"1815","DOI":"10.1007\/s00521-021-06804-y","volume":"34","author":"P Oza","year":"2022","unstructured":"Oza P, Sharma P, Patel S, Kumar P (2022) Deep convolutional neural networks for computer-aided breast cancer diagnostic: a survey. Neural Comput Appl 34:1815\u20131836. https:\/\/doi.org\/10.1007\/s00521-021-06804-y","journal-title":"Neural Comput Appl"},{"key":"9165_CR17","doi-asserted-by":"crossref","unstructured":"Guan S, Loew M (2017) Breast cancer detection using transfer learning in convolutional neural networks. In: 2017 IEEE applied imagery pattern recognition workshop (AIPR), pp 1\u20138. IEEE","DOI":"10.1109\/AIPR.2017.8457948"},{"issue":"5","key":"9165_CR18","doi-asserted-by":"publisher","first-page":"5029","DOI":"10.3934\/mbe.2021256","volume":"18","author":"MM Altaf","year":"2021","unstructured":"Altaf MM (2021) A hybrid deep learning model for breast cancer diagnosis based on transfer learning and pulse-coupled neural networks. Math Biosci Eng 18(5):5029\u20135046","journal-title":"Math Biosci Eng"},{"issue":"3","key":"9165_CR19","doi-asserted-by":"publisher","first-page":"876","DOI":"10.3390\/s22030876","volume":"22","author":"M Alruwaili","year":"2022","unstructured":"Alruwaili M, Gouda W (2022) Automated breast cancer detection models based on transfer learning. Sensors 22(3):876","journal-title":"Sensors"},{"issue":"2","key":"9165_CR20","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1002\/ima.22400","volume":"30","author":"Z Sha","year":"2020","unstructured":"Sha Z, Hu L, Rouyendegh BD (2020) Deep learning and optimization algorithms for automatic breast cancer detection. Int J Imaging Syst Technol 30(2):495\u2013506","journal-title":"Int J Imaging Syst Technol"},{"key":"9165_CR21","doi-asserted-by":"publisher","first-page":"71194","DOI":"10.1109\/ACCESS.2021.3079204","volume":"9","author":"A Saber","year":"2021","unstructured":"Saber A, Sakr M, Abo-Seida OM, Keshk A, Chen H (2021) A novel deep-learning model for automatic detection and classification of breast cancer using the transfer-learning technique. IEEE Access 9:71194\u201371209","journal-title":"IEEE Access"},{"key":"9165_CR22","doi-asserted-by":"crossref","unstructured":"Carneiro G, Nascimento J, Bradley AP (2015) Unregistered multiview mammogram analysis with pre-trained deep learning models. In: International conference on medical image computing and computer-assisted intervention, pp 652\u2013660. Springer","DOI":"10.1007\/978-3-319-24574-4_78"},{"key":"9165_CR23","doi-asserted-by":"crossref","unstructured":"Shams S, Platania R, Zhang J, Kim J, Lee K, Park SJ (2018) Deep generative breast cancer screening and diagnosis. In: International conference on medical image computing and computer-assisted intervention, pp 859\u2013867. Springer","DOI":"10.1007\/978-3-030-00934-2_95"},{"issue":"7","key":"9165_CR24","doi-asserted-by":"publisher","first-page":"1238","DOI":"10.3390\/diagnostics13071238","volume":"13","author":"KEA Jabeen","year":"2023","unstructured":"Jabeen KEA (2023) Bc2netrf: breast cancer classification from mammogram images using enhanced deep learning features and equilibrium-jaya controlled regula falsi-based features selection. Diagnostics 13(7):1238","journal-title":"Diagnostics"},{"key":"9165_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104534","volume":"81","author":"F Atban","year":"2023","unstructured":"Atban F, Ekinci E, Garip Z (2023) Traditional machine learning algorithms for breast cancer image classification with optimized deep features. Biomed Signal Process Control 81:104534","journal-title":"Biomed Signal Process Control"},{"key":"9165_CR26","doi-asserted-by":"crossref","unstructured":"Pereira JMS, de Santana MA, de Lima CL, de Lima RdCF., de Lima SML, dos Santos WP (2023) Feature selection based on dialectical optimization algorithm for breast lesion classification in thermographic images. In: Research anthology on medical informatics in breast and cervical cancer, pp 96\u2013119. IGI Global (2023)","DOI":"10.4018\/978-1-6684-7136-4.ch005"},{"issue":"7","key":"9165_CR27","doi-asserted-by":"publisher","first-page":"8048","DOI":"10.1007\/s11227-022-04992-5","volume":"79","author":"P Oza","year":"2023","unstructured":"Oza P, Sharma P, Patel S (2023) Deep ensemble transfer learning-based framework for mammographic image classification. J Supercomput 79(7):8048\u20138069","journal-title":"J Supercomput"},{"key":"9165_CR28","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.media.2018.03.006","volume":"47","author":"A Hamidinekoo","year":"2018","unstructured":"Hamidinekoo A, Denton E, Rampun A, Honnor K, Zwiggelaar R (2018) Deep learning in mammography and breast histology, an overview and future trends. Med Image Anal 47:45\u201367","journal-title":"Med Image Anal"},{"key":"9165_CR29","doi-asserted-by":"crossref","unstructured":"Oza P, Shah Y, Vegda M (2022) A comprehensive study of mammogram classification techniques. In: Tracking and preventing diseases with artificial intelligence, pp 217\u2013238. Springer (2022)","DOI":"10.1007\/978-3-030-76732-7_10"},{"key":"9165_CR30","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1016\/j.cmpb.2015.12.014","volume":"127","author":"J Arevalo","year":"2016","unstructured":"Arevalo J, Gonz\u00e1lez FA, Ramos-Poll\u00e1n R, Oliveira JL, Lopez MAG (2016) Representation learning for mammography mass lesion classification with convolutional neural networks. Comput Methods Programs Biomed 127:248\u2013257","journal-title":"Comput Methods Programs Biomed"},{"issue":"3","key":"9165_CR31","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.3.3.034501","volume":"3","author":"BQ Huynh","year":"2016","unstructured":"Huynh BQ, Li H, Giger ML (2016) Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J Med Imaging 3(3):034501","journal-title":"J Med Imaging"},{"key":"9165_CR32","unstructured":"L\u00e9vy D, Jain A (2016) Breast mass classification from mammograms using deep convolutional neural networks. arXiv preprint arXiv:1612.00542"},{"issue":"3","key":"9165_CR33","doi-asserted-by":"publisher","first-page":"115","DOI":"10.12694\/scpe.v23i3.1992","volume":"23","author":"P Oza","year":"2022","unstructured":"Oza P, Sharma P, Patel S (2022) Transfer learning assisted classification of artefacts removed and contrast improved digital mammograms. Scalable Comput Pract Exp 23(3):115\u2013127","journal-title":"Scalable Comput Pract Exp"},{"issue":"2","key":"9165_CR34","doi-asserted-by":"publisher","first-page":"51","DOI":"10.12694\/scpe.v23i2.1975","volume":"23","author":"P Oza","year":"2022","unstructured":"Oza P, Sharma P, Patel S (2022) A transfer representation learning approach for breast cancer diagnosis from mammograms using efficientnet models. Scalable Comput Pract Exp 23(2):51\u201358","journal-title":"Scalable Comput Pract Exp"},{"key":"9165_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patrec.2019.03.022","volume":"125","author":"S Khan","year":"2019","unstructured":"Khan S, Islam N, Jan Z, Din IU, Rodrigues JJC (2019) A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recog Lett 125:1\u20136","journal-title":"Pattern Recog Lett"},{"key":"9165_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103774","volume":"121","author":"R Agarwal","year":"2020","unstructured":"Agarwal R, D\u00edaz O, Yap MH, Llado X, Marti R (2020) Deep learning for mass detection in full field digital mammograms. Comput Biol Med 121:103774","journal-title":"Comput Biol Med"},{"key":"9165_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2021.100551","volume":"23","author":"AV Vedalankar","year":"2021","unstructured":"Vedalankar AV, Gupta SS, Manthalkar RR (2021) Addressing architectural distortion in mammogram using alexnet and support vector machine. Inform Med Unlocked 23:100551","journal-title":"Inform Med Unlocked"},{"key":"9165_CR38","first-page":"4163","volume":"33","author":"I Kim","year":"2020","unstructured":"Kim I, Kim Y, Kim S (2020) Learning loss for test-time augmentation. Adv Neural Inf Process Syst 33:4163\u20134174","journal-title":"Adv Neural Inf Process Syst"},{"key":"9165_CR39","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention, pp 234\u2013241. Springer","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"9165_CR40","doi-asserted-by":"crossref","unstructured":"Isensee F, Petersen J, Klein A, Zimmerer D, Jaeger PF, Kohl S, Wasserthal J, Koehler G, Norajitra T, Wirkert S et al (2018) nnu-net: self-adapting framework for u-net-based medical image segmentation. arXiv preprint arXiv:1809.10486","DOI":"10.1007\/978-3-658-25326-4_7"},{"key":"9165_CR41","unstructured":"Pang T, Xu K, Zhu J (2019) Mixup inference: better exploiting mixup to defend adversarial attacks. arXiv preprint arXiv:1909.11515"},{"key":"9165_CR42","unstructured":"Suckling J, Parker J, Dance D, Astley S, Hutt I, Boggis C, Ricketts I, Stamatakis E, Cerneaz N, Kok S, et al (2015) Mammographic image analysis society (mias) database v1. 21"},{"issue":"1","key":"9165_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2017.177","volume":"4","author":"RS Lee","year":"2017","unstructured":"Lee RS, Gimenez F, Hoogi A, Miyake KK, Gorovoy M, Rubin DL (2017) A curated mammography data set for use in computer-aided detection and diagnosis research. Sci Data 4(1):1\u20139","journal-title":"Sci Data"},{"key":"9165_CR44","doi-asserted-by":"crossref","unstructured":"Heath M, Bowyer K, Kopans D, Kegelmeyer P, Moore R, Chang K, Munishkumaran S (1998) Current status of the digital database for screening mammography. In: Digital mammography, pp 457\u2013460. Springer","DOI":"10.1007\/978-94-011-5318-8_75"},{"key":"9165_CR45","doi-asserted-by":"crossref","unstructured":"He K, Sun J (2015) Convolutional neural networks at constrained time cost. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5353\u20135360 (2015)","DOI":"10.1109\/CVPR.2015.7299173"},{"key":"9165_CR46","unstructured":"Srivastava RK, Greff K, Schmidhuber J (2015) Highway networks. arXiv preprint arXiv:1505.00387"},{"key":"9165_CR47","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"9165_CR48","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging8050141","author":"P Oza","year":"2022","unstructured":"Oza P, Sharma P, Patel S, Adedoyin F, Bruno A (2022) Image augmentation techniques for mammogram analysis. J Imaging. https:\/\/doi.org\/10.3390\/jimaging8050141","journal-title":"J Imaging"},{"issue":"1","key":"9165_CR49","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1109\/TCYB.2015.2401732","volume":"46","author":"K Gu","year":"2015","unstructured":"Gu K, Zhai G, Lin W, Liu M (2015) The analysis of image contrast: from quality assessment to automatic enhancement. IEEE Trans Cybern 46(1):284\u2013297","journal-title":"IEEE Trans Cybern"},{"key":"9165_CR50","first-page":"1","volume":"71","author":"H Xing","year":"2022","unstructured":"Xing H, Xiao Z, Qu R, Zhu Z, Zhao B (2022) An efficient federated distillation learning system for multitask time series classification. IEEE Trans Instrum Meas 71:1\u201312","journal-title":"IEEE Trans Instrum Meas"},{"issue":"11","key":"9165_CR51","doi-asserted-by":"publisher","first-page":"8583","DOI":"10.1002\/int.22957","volume":"37","author":"H Xing","year":"2022","unstructured":"Xing H, Xiao Z, Zhan D, Luo S, Dai P, Li K (2022) Selfmatch: robust semisupervised time-series classification with self-distillation. Int J Intell Syst 37(11):8583\u20138610","journal-title":"Int J Intell Syst"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09165-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09165-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09165-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T18:20:05Z","timestamp":1704997205000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09165-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,20]]},"references-count":51,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["9165"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09165-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,20]]},"assertion":[{"value":"6 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 November 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}