{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T10:28:24Z","timestamp":1771064904411,"version":"3.50.1"},"reference-count":27,"publisher":"Walter de Gruyter GmbH","issue":"2","license":[{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Breast Cancer diagnosis is one of the most studied problems in the medical domain. Cancer diagnosis has been studied extensively, which instantiates the need for early prediction of cancer disease. To obtain advance prediction, health records are exploited and given as input to an automated system. The paper focuses on constructing an automated system by employing deep learning based recurrent neural network models. A stacked GRU-LSTM-BRNN is proposed in this paper that accepts health records of a patient for determining the possibility of being affected by breast cancer. The proposed model is compared against other baseline classifiers such as stacked simple-RNN model, stacked LSTM-RNN model, stacked GRU-RNN model. Comparative results obtained in this study indicate that the stacked GRU-LSTM-BRNN model yields better classification performance for predictions related to breast cancer disease.<\/jats:p>","DOI":"10.2478\/acss-2020-0018","type":"journal-article","created":{"date-parts":[[2021,1,13]],"date-time":"2021-01-13T08:07:08Z","timestamp":1610525228000},"page":"163-171","source":"Crossref","is-referenced-by-count":18,"title":["Breast Cancer Prediction Using Stacked GRU-LSTM-BRNN"],"prefix":"10.2478","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8557-0376","authenticated-orcid":false,"given":"Shawni","family":"Dutta","sequence":"first","affiliation":[{"name":"Department of Computer Science , The Bhawanipur Education Society College , Kolkata , India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9447-647X","authenticated-orcid":false,"given":"Jyotsna Kumar","family":"Mandal","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Engineering , University of Kalyani , Kalyani , West Bengal , India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7823-5457","authenticated-orcid":false,"given":"Tai Hoon","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Economics and Management , Beijing Jiaotong University , China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4868-3459","authenticated-orcid":false,"given":"Samir Kumar","family":"Bandyopadhyay","sequence":"additional","affiliation":[{"name":"Department of Computer Science , The Bhawanipur Education Society College , Kolkata , India"}]}],"member":"374","published-online":{"date-parts":[[2020,12,28]]},"reference":[{"key":"2025012623043453515_j_acss-2020-0018_ref_001_w2aab3b7c17b1b6b1ab1ab1Aa","doi-asserted-by":"crossref","unstructured":"[1] M. Sibbering and C.-A. Courtney, \u201cManagement of breast cancer: Basic principles,\u201d Surgery (Oxford), vol. 34, no. 1, pp. 25\u201331, Jan. 2016. https:\/\/doi.org\/10.1016\/j.mpsur.2015.10.00510.1016\/j.mpsur.2015.10.005","DOI":"10.1016\/j.mpsur.2015.10.005"},{"key":"2025012623043453515_j_acss-2020-0018_ref_002_w2aab3b7c17b1b6b1ab1ab2Aa","doi-asserted-by":"crossref","unstructured":"[2] J. Sathwara, S. Bobdey, and B. Ganesh, \u201cBreast cancer survival studies in India: A review,\u201d International Journal of Research in Medical Sciences, vol. 4, no. 8, pp. 3102\u20133108, Aug. 2016. https:\/\/doi.org\/10.18203\/2320-6012.ijrms2016226610.18203\/2320-6012.ijrms20162266","DOI":"10.18203\/2320-6012.ijrms20162266"},{"key":"2025012623043453515_j_acss-2020-0018_ref_003_w2aab3b7c17b1b6b1ab1ab3Aa","doi-asserted-by":"crossref","unstructured":"[3] C. K. Anders, R. Johnson, J. Litton, M. Phillips, and A. Bleyer, \u201cBreast cancer before age 40 years,\u201d Seminars in Oncology, vol. 36, no. 3, pp. 237\u2013249, Jun. 2009. https:\/\/doi.org\/10.1053\/j.seminoncol.2009.03.00110.1053\/j.seminoncol.2009.03.001289402819460581","DOI":"10.1053\/j.seminoncol.2009.03.001"},{"key":"2025012623043453515_j_acss-2020-0018_ref_004_w2aab3b7c17b1b6b1ab1ab4Aa","doi-asserted-by":"crossref","unstructured":"[4] \u00d3.Marb\u00e1n, G. Mariscal, and J. Segovia, \u201cA data mining & knowledge discovery process model,\u201d in Data Mining and Knowledge Discovery in Real Life Applications, (J. Ponce and A.Karahoca, Eds.). In Tech, 2009. https:\/\/doi.org\/10.5772\/643810.5772\/6438","DOI":"10.5772\/6438"},{"key":"2025012623043453515_j_acss-2020-0018_ref_005_w2aab3b7c17b1b6b1ab1ab5Aa","doi-asserted-by":"crossref","unstructured":"[5] D. Shen, G. Wu, and H.-I. Suk, \u201cDeep learning in medical image analysis,\u201d Annual Review of Biomedical Engineering, vol. 19, pp. 221\u2013248, Jun. 2017. https:\/\/doi.org\/10.1146\/annurev-bioeng-071516-04444210.1146\/annurev-bioeng-071516-044442547972228301734","DOI":"10.1146\/annurev-bioeng-071516-044442"},{"key":"2025012623043453515_j_acss-2020-0018_ref_006_w2aab3b7c17b1b6b1ab1ab6Aa","doi-asserted-by":"crossref","unstructured":"[6] A. Sherstinsky, \u201cFundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network,\u201d Physica D: Nonlinear Phenomena, vol. 404, article number 132306, Mar. 2020. https:\/\/doi.org\/10.1016\/j.physd.2019.13230610.1016\/j.physd.2019.132306","DOI":"10.1016\/j.physd.2019.132306"},{"key":"2025012623043453515_j_acss-2020-0018_ref_007_w2aab3b7c17b1b6b1ab1ab7Aa","unstructured":"[7] J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, \u201cEmpirical evaluation of gated recurrent neural networks on sequence modeling,\u201d 2014. [Online]. Available: https:\/\/arxiv.org\/abs\/1412.3555"},{"key":"2025012623043453515_j_acss-2020-0018_ref_008_w2aab3b7c17b1b6b1ab1ab8Aa","doi-asserted-by":"crossref","unstructured":"[8] D. Soutner and L. M\u00fcller, \u201cApplication of LSTM neural networks in language modelling,\u201d in Habernal I., Matou\u0161ek V. (eds) Text, Speech, and Dialogue. TSD 2013. Lecture Notes in Computer Science, vol. 8082. Springer, Berlin, Heidelberg. https:\/\/doi.org\/10.1007\/978-3-642-40585-3_1410.1007\/978-3-642-40585-3_14","DOI":"10.1007\/978-3-642-40585-3_14"},{"key":"2025012623043453515_j_acss-2020-0018_ref_009_w2aab3b7c17b1b6b1ab1ab9Aa","doi-asserted-by":"crossref","unstructured":"[9] M. A. Mohammed, B. Al-Khateeb, A. N. Rashid, D. A. Ibrahim, M. K. Abd Ghani, and S. A. Mostafa, \u201cNeural network and multi-fractal dimension features for breast cancer classification from ultrasound images,\u201d Computers & Electrical Engineering, vol. 70, pp. 871\u2013882, Aug. 2018. https:\/\/doi.org\/10.1016\/j.compeleceng.2018.01.03310.1016\/j.compeleceng.2018.01.033","DOI":"10.1016\/j.compeleceng.2018.01.033"},{"key":"2025012623043453515_j_acss-2020-0018_ref_010_w2aab3b7c17b1b6b1ab1ac10Aa","doi-asserted-by":"crossref","unstructured":"[10] R. Delshi Howsalya Devi and P. Deepika, \u201cPerformance comparison of various clustering techniques for diagnosis of breast cancer,\u201d in 2015 IEEE International Conference on Computational Intelligence and Computing Research, IEEE, 2016. https:\/\/doi.org\/10.1109\/ICCIC.2015.743571110.1109\/ICCIC.2015.7435711","DOI":"10.1109\/ICCIC.2015.7435711"},{"key":"2025012623043453515_j_acss-2020-0018_ref_011_w2aab3b7c17b1b6b1ab1ac11Aa","doi-asserted-by":"crossref","unstructured":"[11] F. F. Ting, Y. J. Tan, and K. S. Sim, \u201cConvolutional neural network improvement for breast cancer classification,\u201d Expert Systems with Applications, vol. 120, pp. 103\u2013115, Apr. 2019. https:\/\/doi.org\/10.1016\/j.eswa.2018.11.00810.1016\/j.eswa.2018.11.008","DOI":"10.1016\/j.eswa.2018.11.008"},{"key":"2025012623043453515_j_acss-2020-0018_ref_012_w2aab3b7c17b1b6b1ab1ac12Aa","doi-asserted-by":"crossref","unstructured":"[12] P. J. Sudharshan, C. Petitjean, F. Spanhol, L. E. Oliveira, L. Heutte, and P. Honeine, \u201cMultiple instance learning for histopathological breast cancer image classification,\u201d Expert Systems with Applications, vol. 117, pp. 103\u2013111, Mar. 2019. https:\/\/doi.org\/10.1016\/j.eswa.2018.09.04910.1016\/j.eswa.2018.09.049","DOI":"10.1016\/j.eswa.2018.09.049"},{"key":"2025012623043453515_j_acss-2020-0018_ref_013_w2aab3b7c17b1b6b1ab1ac13Aa","doi-asserted-by":"crossref","unstructured":"[13] Shallu and R. Mehra, \u201cBreast cancer histology images classification: Training from scratch or transfer learning?\u201d ICT Express, vol. 4, no. 4, pp. 247\u2013254, Dec. 2018. https:\/\/doi.org\/10.1016\/j.icte.2018.10.00710.1016\/j.icte.2018.10.007","DOI":"10.1016\/j.icte.2018.10.007"},{"key":"2025012623043453515_j_acss-2020-0018_ref_014_w2aab3b7c17b1b6b1ab1ac14Aa","doi-asserted-by":"crossref","unstructured":"[14] S. Kaymak, A. Helwan, and D. Uzun, \u201cBreast cancer image classification using artificial neural networks,\u201d Procedia Computer Science, vol. 120, pp. 126\u2013131, 2017. https:\/\/doi.org\/10.1016\/j.procs.2017.11.21910.1016\/j.procs.2017.11.219","DOI":"10.1016\/j.procs.2017.11.219"},{"key":"2025012623043453515_j_acss-2020-0018_ref_015_w2aab3b7c17b1b6b1ab1ac15Aa","unstructured":"[15] V. Chaurasia and S. Pal, \u201cData mining techniques: To predict and resolve breast cancer survivability,\u201d International Journal of Computer Science and Mobile Computing, vol. 3, no. 1, pp. 10\u201322, Jan. 2014."},{"key":"2025012623043453515_j_acss-2020-0018_ref_016_w2aab3b7c17b1b6b1ab1ac16Aa","doi-asserted-by":"crossref","unstructured":"[16] S. A. Medjahed, T. A.Saadi, and A. Benyettou, \u201cBreast cancer diagnosis by using k-nearest neighbor with different distances and classification rules,\u201d International Journal of Computer Applications, vol. 62, no. 1, pp. 1\u20135, Jan. 2013. https:\/\/doi.org\/10.5120\/10041-463510.5120\/10041-4635","DOI":"10.5120\/10041-4635"},{"key":"2025012623043453515_j_acss-2020-0018_ref_017_w2aab3b7c17b1b6b1ab1ac17Aa","doi-asserted-by":"crossref","unstructured":"[17] D. Delen, G. Walker, and A. Kadam, \u201cPredicting breast cancer survivability: A comparison of three data mining methods,\u201d Artificial Intelligence in Medicine, vol. 34, no. 2, pp. 113\u2013127, Jun. 2005. https:\/\/doi.org\/10.1016\/j.artmed.2004.07.00210.1016\/j.artmed.2004.07.00215894176","DOI":"10.1016\/j.artmed.2004.07.002"},{"key":"2025012623043453515_j_acss-2020-0018_ref_018_w2aab3b7c17b1b6b1ab1ac18Aa","unstructured":"[18] L. G. Ahmad, A. T.Eshlaghy, A. Poorebrahimi, M. Ebrahimi, and A. R. Razavi, \u201cUsing three machine learning techniques for predicting breast cancer recurrence,\u201d Journal of Health & Medical Informatics, vol. 4, no. 2, 2013. https:\/\/doi.org\/10.4172\/2157-7420.100012410.4172\/2157-7420.1000124"},{"key":"2025012623043453515_j_acss-2020-0018_ref_019_w2aab3b7c17b1b6b1ab1ac19Aa","unstructured":"[19] C. Nwankpa, W. Ijomah, A. Gachagan, and S. Marshall, \u201cActivation functions: Comparison of trends in practice and research for deep learning,\u201d 2018. [Online]. Available: https:\/\/arxiv.org\/abs\/1811.03378"},{"key":"2025012623043453515_j_acss-2020-0018_ref_020_w2aab3b7c17b1b6b1ab1ac20Aa","unstructured":"[20] Z. C. Lipton, J. Berkowitz, and C. Elkan, \u201cA critical review of recurrent neural networks for sequence learning,\u201d 2015. [Online]. Available: https:\/\/arxiv.org\/abs\/1506.00019"},{"key":"2025012623043453515_j_acss-2020-0018_ref_021_w2aab3b7c17b1b6b1ab1ac21Aa","doi-asserted-by":"crossref","unstructured":"[21] \u00dc. Budak, Z. C\u00f6mert, Z. N. Rashid, A. \u015eeng\u00fcr, and M. \u00c7\u0131buk, \u201cComputeraided diagnosis system combining FCN and Bi-LSTM model for efficient breast cancer detection from histopathological images,\u201d Applied Soft Computing, vol. 85, article number 105765, Dec. 2019. https:\/\/doi.org\/10.1016\/j.asoc.2019.10576510.1016\/j.asoc.2019.105765","DOI":"10.1016\/j.asoc.2019.105765"},{"key":"2025012623043453515_j_acss-2020-0018_ref_022_w2aab3b7c17b1b6b1ab1ac22Aa","doi-asserted-by":"crossref","unstructured":"[22] Y. You, J. Hseu, C. Ying, J. Demmel, K. Keutzer, and C.-J. Hsieh, \u201cLargebatch training for LSTM and beyond,\u201d in International Conference for High Performance Computing, Networking, Storage and Analysis, 2019. https:\/\/doi.org\/10.1145\/3295500.335613710.1145\/3295500.3356137","DOI":"10.1145\/3295500.3356137"},{"key":"2025012623043453515_j_acss-2020-0018_ref_023_w2aab3b7c17b1b6b1ab1ac23Aa","unstructured":"[23] K. Janocha and W. M. Czarnecki, \u201cOn loss functions for deep neural networks in classification,\u201d SchedaeInformaticae, vol. 25, pp. 49\u201359, 2016. https:\/\/doi.org\/10.4467\/20838476SI.16.004.618510.4467\/20838476SI.16.004.6185"},{"key":"2025012623043453515_j_acss-2020-0018_ref_024_w2aab3b7c17b1b6b1ab1ac24Aa","unstructured":"[24] D. P. Kingma and J. L. Ba, \u201cAdam: A method for stochastic optimization,\u201d in 3rd International Conference on Learning Representations, 2015. [Online]. Available: https:\/\/arxiv.org\/abs\/1412.6980"},{"key":"2025012623043453515_j_acss-2020-0018_ref_025_w2aab3b7c17b1b6b1ab1ac25Aa","unstructured":"[25] D. Dua, and C. Graff, UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science. [Online]. Available: https:\/\/archive.ics.uci.edu\/ml"},{"key":"2025012623043453515_j_acss-2020-0018_ref_026_w2aab3b7c17b1b6b1ab1ac26Aa","unstructured":"[26] F. Cholletet et al., Keras, 2015. [Online.] Available: https:\/\/keras.io"},{"key":"2025012623043453515_j_acss-2020-0018_ref_027_w2aab3b7c17b1b6b1ab1ac27Aa","unstructured":"[27] M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, \u201cTensorFlow: A system for large-scale machine learning,\u201d in 12th USENIX Symposium on Operating Systems Design and Implementation, 2016, pp. 265\u2013283."}],"container-title":["Applied Computer Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/content.sciendo.com\/view\/journals\/acss\/25\/2\/article-p163.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.sciendo.com\/pdf\/10.2478\/acss-2020-0018","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,26]],"date-time":"2025-01-26T23:04:45Z","timestamp":1737932685000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.sciendo.com\/article\/10.2478\/acss-2020-0018"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,1]]},"references-count":27,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,12,28]]},"published-print":{"date-parts":[[2020,12,1]]}},"alternative-id":["10.2478\/acss-2020-0018"],"URL":"https:\/\/doi.org\/10.2478\/acss-2020-0018","relation":{},"ISSN":["2255-8691"],"issn-type":[{"value":"2255-8691","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,1]]}}}