{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T20:45:10Z","timestamp":1770151510139,"version":"3.49.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T00:00:00Z","timestamp":1711497600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T00:00:00Z","timestamp":1711497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61671480 and 62372468"],"award-info":[{"award-number":["61671480 and 62372468"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61671480 and 62372468"],"award-info":[{"award-number":["61671480 and 62372468"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Natural Science Foundation of Shandong Province, China","award":["ZR2019MF073, ZR2023MF008 and ZR2023ZD32"],"award-info":[{"award-number":["ZR2019MF073, ZR2023MF008 and ZR2023ZD32"]}]},{"name":"Natural Science Foundation of Shandong Province, China","award":["ZR2019MF073, ZR2023MF008 and ZR2023ZD32"],"award-info":[{"award-number":["ZR2019MF073, ZR2023MF008 and ZR2023ZD32"]}]},{"DOI":"10.13039\/501100014761","name":"Qingdao Natural Science Foundation","doi-asserted-by":"crossref","award":["23-2-1-161-zyyd-jch"],"award-info":[{"award-number":["23-2-1-161-zyyd-jch"]}],"id":[{"id":"10.13039\/501100014761","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100014761","name":"Qingdao Natural Science Foundation","doi-asserted-by":"crossref","award":["23-2-1-161-zyyd-jch"],"award-info":[{"award-number":["23-2-1-161-zyyd-jch"]}],"id":[{"id":"10.13039\/501100014761","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s00530-024-01295-y","type":"journal-article","created":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T07:03:46Z","timestamp":1711523026000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Design of integrated interactive system for pre-diagnosis of breast cancer pathological images based on CNN and PyQt5"],"prefix":"10.1007","volume":"30","author":[{"given":"Yunkai","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qijia","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weifeng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baodi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,3,27]]},"reference":[{"key":"1295_CR1","doi-asserted-by":"crossref","unstructured":"Akhil, M., Kumar, PVS.: \"Breast Cancer Prognosis using Machine Learning Applications,\" 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), Greater Noida, India, pp. 488\u2013493 (2022)","DOI":"10.1109\/ICAC3N56670.2022.10074517"},{"key":"1295_CR2","first-page":"1911","volume":"11","author":"MY Shang","year":"2020","unstructured":"Shang, M.Y., Guo, S., Zhang, Q., Pu, H.Z.: Breast cancer screening in China. J. Pract. Cancer 11, 1911\u20131914 (2020)","journal-title":"J. Pract. Cancer"},{"issue":"S2","key":"1295_CR3","first-page":"362","volume":"49","author":"Z Xike","year":"2022","unstructured":"Xike, Z., Zhiqing, M., Wenhua, Z., et al.: A review of breast cancer histopathological image classification based on convolutional neural networks. Comput. Sci. 49(S2), 362\u2013370 (2022)","journal-title":"Comput. Sci."},{"key":"1295_CR4","doi-asserted-by":"crossref","unstructured":"Geetha, P. and Umamaheswari, S.: \"Medical imaging modalities and deep learning algorithm for detecting breast cancer at the early stages,\" 2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), Chennai, India pp. 1\u20138. (2022)","DOI":"10.1109\/ICSES55317.2022.9914335"},{"issue":"7","key":"1295_CR5","doi-asserted-by":"publisher","first-page":"1455","DOI":"10.1109\/TBME.2015.2496264","volume":"63","author":"FA Spanhol","year":"2016","unstructured":"Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: A dataset for breast cancer histopathological image classification. IEEE Trans. Biomed. Eng. 63(7), 1455\u20131462 (2016)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"1295_CR6","doi-asserted-by":"publisher","DOI":"10.3233\/XST-200658","author":"Y-L Hou","year":"2020","unstructured":"Hou, Y.-L.: Breast cancer pathological image classification based on deep learning. J X-ray Sci. Technol. (2020). https:\/\/doi.org\/10.3233\/XST-200658","journal-title":"J X-ray Sci. Technol."},{"key":"1295_CR7","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, I.U., Rodrigues, J.J.P.C.: A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Patt. Recogn. Lett. 125, 1\u20136 (2019)","journal-title":"Patt. Recogn. Lett."},{"key":"1295_CR8","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/s42979-023-02413-9","volume":"5","author":"JAA Jothi","year":"2024","unstructured":"Jothi, J.A.A., Damania, K.: DIRXNet: A Hybrid Deep Network for Classification of Breast Histopathology Images. SN Comput. Sci. 5, 77 (2024)","journal-title":"SN Comput. Sci."},{"key":"1295_CR9","volume-title":"Image analysis and recognition iciar 2018 lecture notes in computer science","author":"Y Wang","year":"2018","unstructured":"Wang, Y., Sun, L., Ma, K., Fang, J.: Breast cancer microscope image classification based on CNN with image deformation. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds.) Image analysis and recognition iciar 2018 lecture notes in computer science. Springer, Cham (2018)"},{"key":"1295_CR10","volume-title":"Image Analysis and Recognition ICIAR 2018 Lecture Notes in Computer Science","author":"K Nazeri","year":"2018","unstructured":"Nazeri, K., Aminpour, A., Ebrahimi, M.: Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds.) Image Analysis and Recognition ICIAR 2018 Lecture Notes in Computer Science. Springer, Cham (2018)"},{"key":"1295_CR11","doi-asserted-by":"crossref","unstructured":"Vesal, S., Ravikumar, N., Davari, A., Ellmann, S., Maier, A.: Classification of Breast Cancer Histology Images Using Transfer Learning. In: ICIAR 2018: Image Analysis and Recognition. Springer. Cham, pp. 812\u2013819 (2018)","DOI":"10.1007\/978-3-319-93000-8_92"},{"key":"1295_CR12","doi-asserted-by":"publisher","first-page":"106554","DOI":"10.1016\/j.compbiomed.2023.106554","volume":"153","author":"K Loizidou","year":"2023","unstructured":"Loizidou, K., Elia, R., Pitris, C.: Computer-aided breast cancer detection and classification in mammography: a comprehensive review. Comput. Biol. Med. 153, 106554 (2023)","journal-title":"Comput. Biol. Med."},{"key":"1295_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106554","author":"K Loizidou","year":"2023","unstructured":"Loizidou, K., Elia, R., Pitris, C.: Customer success summary: using AI technology to improve access to medical imaging diagnosis for rural health centers. Qingfeng Mi,GE Healthcare. Comput. Biol. Med. (2023). https:\/\/doi.org\/10.1016\/j.compbiomed.2023.106554","journal-title":"Comput. Biol. Med."},{"issue":"03","key":"1295_CR14","first-page":"368","volume":"16","author":"X Chuanbo","year":"2019","unstructured":"Chuanbo, X., Qin, M., Hong, L.: The value of artificial intelligence ultrasound in the diagnosis and prognosis of breast cancer. Chin. J. Mater. Child Clin. Med. (Electronic Edition) 16(03), 368\u2013372 (2019)","journal-title":"Chin. J. Mater. Child Clin. Med. (Electronic Edition)"},{"key":"1295_CR15","first-page":"309","volume":"2021","author":"X Yang","year":"2021","unstructured":"Yang, X., Yang, D., Huang, C.: An interactive prediction system of breast cancer based on ResNet50, chatbot and PyQt,\", 2021 2nd International Seminar on Artificial Intelligence. Netw. Inform. Technol. (AINIT) Shanghai, China 2021, 309\u2013316 (2021)","journal-title":"Netw. Inform. Technol. (AINIT) Shanghai, China"},{"key":"1295_CR16","unstructured":"Simonyan, K. and Zisserman, A.: \u201cVery Deep Convolutional Networks for Large-Scale Image Recognition.\u201d CoRR abs\/1409.1556. (2014)"},{"key":"1295_CR17","doi-asserted-by":"publisher","DOI":"10.47836\/pjst.28.s2.13","author":"A Ismail","year":"2020","unstructured":"Ismail, A., Ahmad, S.A., Soh, A.C., et al.: Deep learning object detector using a combination of Convolutional Neural Network (CNN) Architecture (MiniVGGNet) and classic object detection algorithm. JST (2020). https:\/\/doi.org\/10.47836\/pjst.28.s2.13","journal-title":"JST"},{"key":"1295_CR18","first-page":"1","volume-title":"Machine intelligence and emerging technologies MIET 2022 lecture notes of the institute for computer sciences social informatics and telecommunications engineering","author":"NA Roni","year":"2023","unstructured":"Roni, N.A., Hossain, M., Hossain, M.B., Efat, M.A., Yousuf, M.A.: Deep convolutional comparison architecture for breast cancer binary classification. In: Satu, M.S., Moni, M.A., Kaiser, M.S., Arefin, M.S. (eds.) Machine intelligence and emerging technologies MIET 2022 lecture notes of the institute for computer sciences social informatics and telecommunications engineering, pp. 1\u20132. Springer, Cham (2023)"},{"key":"1295_CR19","first-page":"1","volume":"2023","author":"CR Prasad","year":"2023","unstructured":"Prasad, C.R., Arun, B., Amulya, S., Abboju, P., Kollem, S., Yalabaka, S.: Breast cancer classification using CNN with transfer learning models. Int. Conf. Adv. Technol. (ICONAT) Goa, India 2023, 1\u20135 (2023)","journal-title":"Int. Conf. Adv. Technol. (ICONAT) Goa, India"},{"key":"1295_CR20","doi-asserted-by":"crossref","unstructured":"HAhmad, H.M., Ghuffar, S. and Khurshid, K.: \"Classification of Breast Cancer Histology Images Using Transfer Learning,\" 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Islamabad, Pakistan, pp. 328\u2013332. (2019)","DOI":"10.1109\/IBCAST.2019.8667221"},{"key":"1295_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M. and Sun, J.: \"ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices,\" 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp. 6848\u20136856. (2018)","DOI":"10.1109\/CVPR.2018.00716"},{"key":"1295_CR22","unstructured":"Howard A.G., Zhu M, Chen B, et al.: MobileNets: Efficient convolutional neural networks for mobile vision applications (2017)"},{"key":"1295_CR23","unstructured":"Iandola, F.N., Moskewicz, M.W., Ashraf, K., Han, S., Dally, W.J., Keutzer, K.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size. ArXiv, abs\/1602.07360 (2016)"},{"key":"1295_CR24","doi-asserted-by":"crossref","unstructured":"Zoph, B., Vasudevan, V., Shlens, J. and Le, Q.V.: \"Learning Transferable Architectures for Scalable Image Recognition,\" 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, , pp. 8697\u20138710 (2018)","DOI":"10.1109\/CVPR.2018.00907"},{"key":"1295_CR25","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A. and Chen, L.C.,: \"MobileNetV2: Inverted Residuals and Linear Bottlenecks,\" 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"issue":"8","key":"1295_CR26","doi-asserted-by":"publisher","first-page":"981","DOI":"10.3390\/bioengineering10080981.PMID:37627866;PMCID:PMC10451633","volume":"10","author":"Z Riaz","year":"2023","unstructured":"Riaz, Z., Khan, B., Abdullah, S., Khan, S., Islam, M.S.: Lung tumor image segmentation from computer tomography images using MobileNetV2 and transfer learning. Bioengineering (Basel) 10(8), 981 (2023). https:\/\/doi.org\/10.3390\/bioengineering10080981.PMID:37627866;PMCID:PMC10451633","journal-title":"Bioengineering (Basel)"},{"key":"1295_CR27","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: \"ImageNet: A large-scale hierarchical image database,\" 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"1295_CR28","doi-asserted-by":"publisher","first-page":"4461","DOI":"10.1007\/s11063-022-11049-4","volume":"55","author":"AS Qureshi","year":"2023","unstructured":"Qureshi, A.S., Roos, T.: Transfer learning with ensembles of deep neural networks for skin cancer detection in imbalanced data sets. Neural. Process. Lett. 55, 4461\u20134479 (2023)","journal-title":"Neural. Process. Lett."},{"key":"1295_CR29","first-page":"3873","volume":"2018","author":"D Justus","year":"2018","unstructured":"Justus, D., Brennan, J., Bonner, S., McGough, A.S.: Predicting the computational cost of deep learning models. IEEE Int. Conf. Big Data (Big Data) 2018, 3873\u20133882 (2018)","journal-title":"IEEE Int. Conf. Big Data (Big Data)"},{"key":"1295_CR30","first-page":"2086","volume":"2022","author":"H Zhang","year":"2022","unstructured":"Zhang, H., Huang, W., Qi, J.: Design and implementation of object image detection interface system based on PyQt5 and improved SSD algorithm. IEEE 10th Joint Int. Inform. Technol. Artif. Intell. Conf. (ITAIC) Chongqing, China 2022, 2086\u20132090 (2022)","journal-title":"IEEE 10th Joint Int. Inform. Technol. Artif. Intell. Conf. (ITAIC) Chongqing, China"},{"key":"1295_CR31","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/s00354-023-00212-7","volume":"41","author":"C Kanumuri","year":"2023","unstructured":"Kanumuri, C., Chodavarapu, R.M.: GUI enabled optimized approach of CNN for automatic diagnosis of COVID-19 using radiograph images. New Gener. Comput. 41, 213\u2013224 (2023)","journal-title":"New Gener. Comput."},{"key":"1295_CR32","doi-asserted-by":"publisher","first-page":"15600","DOI":"10.1038\/s41598-022-19278-2","volume":"12","author":"Z Hameed","year":"2022","unstructured":"Hameed, Z., Garcia-Zapirain, B., Aguirre, J.J., et al.: Multiclass classification of breast cancer histopathology images using multilevel features of deep convolutional neural network. Sci. Rep. 12, 15600 (2022)","journal-title":"Sci. Rep."},{"key":"1295_CR33","doi-asserted-by":"publisher","first-page":"100508","DOI":"10.1109\/ACCESS.2023.3314978","volume":"11","author":"Y Liang","year":"2023","unstructured":"Liang, Y., Meng, Z.: Brea-Net: an interpretable dual-attention network for imbalanced breast cancer classification. IEEE Access 11, 100508\u2013100517 (2023)","journal-title":"IEEE Access"},{"key":"1295_CR34","unstructured":"Khikani, H.A., Elazab, N., Elgarayhi, A., Elmogy, M.M., Sallah, M. Breast Cancer Classification Based on Histopathological Images Using a Deep Learning Capsule Network. ArXiv, abs\/2208.00594. (2022)"},{"key":"1295_CR35","first-page":"101","volume":"8","author":"S Sharma","year":"2022","unstructured":"Sharma, S., Kumar, S.: The Xception model: a potential feature extractor in breast cancer histology images classification. ICTExpress 8, 101\u2013108 (2022)","journal-title":"ICTExpress"},{"issue":"1","key":"1295_CR36","doi-asserted-by":"publisher","first-page":"752","DOI":"10.1109\/TCBB.2022.3163277","volume":"20","author":"M Saini","year":"2023","unstructured":"Saini, M., Susan, S.: VGGIN-Net: deep transfer network for imbalanced breast cancer dataset. IEEE\/ACM Trans. Comput. Biol. Bioinform. 20(1), 752\u2013762 (2023)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"1295_CR37","doi-asserted-by":"publisher","first-page":"15750","DOI":"10.1109\/ACCESS.2023.3245023","volume":"11","author":"A Ijaz","year":"2023","unstructured":"Ijaz, A., et al.: Modality specific CBAM-VGGNet model for the classification of breast histopathology images via transfer learning. IEEE Access 11, 15750\u201315762 (2023)","journal-title":"IEEE Access"},{"key":"1295_CR38","doi-asserted-by":"publisher","first-page":"6081","DOI":"10.3390\/app13106081","volume":"13","author":"S Tangsakul","year":"2023","unstructured":"Tangsakul, S., Wongthanavasu, S.: Deep cellular automata-based feature extraction for classification of the breast cancer image. Appl. Sci. 13, 6081 (2023)","journal-title":"Appl. Sci."},{"key":"1295_CR39","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-17281-x","author":"A Nogales","year":"2023","unstructured":"Nogales, A., P\u00e9rez-Lara, F., Garc\u00eda-Tejedor, \u00c1.J.: Enhancing breast cancer diagnosis with deep learning and evolutionary algorithms: a comparison of approaches using different thermographic imaging treatments. Multimed. Tools Appl. (2023). https:\/\/doi.org\/10.1007\/s11042-023-17281-x","journal-title":"Multimed. Tools Appl."},{"key":"1295_CR40","doi-asserted-by":"publisher","first-page":"924432","DOI":"10.3389\/fpubh.2022.924432","volume":"10","author":"S Arooj","year":"2022","unstructured":"Arooj, S., Atta-Ur-Rahman, Z.M., Khan, M.F., Alissa, K., Khan, M.A., Mosavi, A.: Breast cancer detection and classification empowered with transfer learning. Front. Public Health 10, 924432 (2022)","journal-title":"Front. Public Health"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01295-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-024-01295-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01295-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T13:13:39Z","timestamp":1712927619000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-024-01295-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,27]]},"references-count":40,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["1295"],"URL":"https:\/\/doi.org\/10.1007\/s00530-024-01295-y","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,27]]},"assertion":[{"value":"12 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"95"}}