{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T07:19:34Z","timestamp":1774163974293,"version":"3.50.1"},"reference-count":16,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T00:00:00Z","timestamp":1703721600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T00:00:00Z","timestamp":1703721600000},"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":["Innovations Syst Softw Eng"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s11334-023-00547-w","type":"journal-article","created":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T13:01:35Z","timestamp":1703768495000},"page":"533-539","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Convolutional neural network-based classifiers for liver tumor detection using computed tomography scans"],"prefix":"10.1007","volume":"21","author":[{"given":"Yagnesh","family":"Challagundla","sequence":"first","affiliation":[]},{"given":"Trilok Sai Charan","family":"Tunuguntla","sequence":"additional","affiliation":[]},{"given":"Sindhu Gayathri","family":"Tunuguntla","sequence":"additional","affiliation":[]},{"given":"Nagaraju","family":"Devarakonda","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,28]]},"reference":[{"key":"547_CR1","unstructured":"Yoshihiro Y et al (2017) Detection of liver tumor candidates from CT images using deep convolutional neural networks. In: International conference on innovation in medicine and healthcare. Springer, Cham"},{"key":"547_CR2","doi-asserted-by":"publisher","first-page":"105620","DOI":"10.1016\/j.compbiomed.2022.105620","volume":"147","author":"Gul Sidra","year":"2022","unstructured":"Sidra Gul et al (2022) Deep learning techniques for liver and liver tumor segmentation: a review. Comput Bio Med 147:105620","journal-title":"Comput Bio Med"},{"key":"547_CR3","doi-asserted-by":"publisher","first-page":"680","DOI":"10.3389\/fonc.2020.00680","volume":"10","author":"S Zhen","year":"2020","unstructured":"Zhen S et al (2020) Deep learning for accurate diagnosis of liver tumor based on magnetic resonance imaging and clinical data. Front Oncol 10:680","journal-title":"Front Oncol"},{"issue":"11","key":"547_CR4","doi-asserted-by":"publisher","first-page":"146","DOI":"10.4236\/jcc.2015.311023","volume":"3","author":"W Li","year":"2015","unstructured":"Li W (2015) Automatic segmentation of liver tumor in CT images with deep convolutional neural networks. J Comput Commun 3(11):146","journal-title":"J Comput Commun"},{"issue":"5","key":"547_CR5","first-page":"1316","volume":"39","author":"Seo Hyunseok","year":"2019","unstructured":"Hyunseok Seo et al (2019) Modified U-Net (mU-Net) with incorporation of object-dependent high level features for improved liver and liver-tumor segmentation in CT images. IEEE Trans Med Imaging 39(5):1316\u20131325","journal-title":"IEEE Trans Med Imaging"},{"issue":"7","key":"547_CR6","doi-asserted-by":"publisher","first-page":"3348","DOI":"10.1007\/s00330-019-06214-8","volume":"29","author":"CJ Wang","year":"2019","unstructured":"Wang CJ et al (2019) Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features. Eur Radiol 29(7):3348\u20133357","journal-title":"Eur Radiol"},{"key":"547_CR7","doi-asserted-by":"crossref","unstructured":"Chintalapati LR et al (2022) Measles rash disease classification based on various CNN classifiers. In: International Conference on Intelligent Systems and Machine Learning. Cham: Springer Nature Switzerland","DOI":"10.1007\/978-3-031-35078-8_2"},{"key":"547_CR8","unstructured":"Abebe AG, Teferi DS (2022) Detection and classification of liver cancers using computed tomography images"},{"key":"547_CR9","doi-asserted-by":"publisher","unstructured":"Challagundla Y, Chintalapati LR, Tunuguntla TSC, Sur A, Roy B, Zhuo ER (2023) Screening of citrus diseases using deep learning embedders and machine learning techniques. In: 3rd International conference on Artificial Intelligence and Signal Processing (AISP). Vijayawada, India 2023:15. https:\/\/doi.org\/10.1109\/AISP57993.2023.10134971","DOI":"10.1109\/AISP57993.2023.10134971"},{"issue":"3","key":"547_CR10","doi-asserted-by":"publisher","first-page":"3185","DOI":"10.1007\/s11042-022-13381-2","volume":"82","author":"M Rela","year":"2023","unstructured":"Rela M, Suryakari NR, Patil RR (2023) A diagnosis system by U-net and deep neural network enabled with optimal feature selection for liver tumor detection using CT images. Multimed Tools Appl 82(3):3185\u20133227","journal-title":"Multimed Tools Appl"},{"issue":"1","key":"547_CR11","first-page":"45","volume":"30","author":"S Hanene","year":"2022","unstructured":"Hanene S, Slama AB, Labidi S (2022) U-Net: a valuable encoder-decoder architecture for liver tumors segmentation in CT images. J Xray Sci Technol 30(1):45\u201356","journal-title":"J Xray Sci Technol"},{"issue":"Suppl 1","key":"547_CR12","doi-asserted-by":"publisher","first-page":"S37","DOI":"10.1016\/j.acra.2020.08.023","volume":"28","author":"MS Reza Syed","year":"2021","unstructured":"Reza Syed MS et al (2021) Deep learning for automated liver segmentation to aid in the study of infectious diseases in nonhuman primates. Acad Radiol 28(Suppl 1):S37\u2013S44. https:\/\/doi.org\/10.1016\/j.acra.2020.08.023","journal-title":"Acad Radiol"},{"issue":"5","key":"547_CR13","doi-asserted-by":"publisher","first-page":"1516","DOI":"10.3390\/s20051516","volume":"20","author":"A Sultan","year":"2020","unstructured":"Sultan A et al (2020) Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5):1516","journal-title":"Sensors"},{"issue":"7","key":"547_CR14","first-page":"96689690","volume":"78","author":"K Anum","year":"2022","unstructured":"Anum K et al (2022) A computer-aided diagnostic system for liver tumor detection using modified U-Net architecture. J Supercomput 78(7):96689690","journal-title":"J Supercomput"},{"issue":"24","key":"547_CR15","doi-asserted-by":"publisher","first-page":"1768","DOI":"10.21037\/atm-21-5822","volume":"9","author":"C Xiaowen","year":"2021","unstructured":"Xiaowen C et al (2021) Liver segmentation in CT imaging with enhanced mask regionbased convolutional neural networks. Ann Transl Med 9(24):1768. https:\/\/doi.org\/10.21037\/atm-21-5822","journal-title":"Ann Transl Med"},{"key":"547_CR16","doi-asserted-by":"crossref","unstructured":"Vijayalakshmi, S et al. (2022) Liver tumor detection using CNN. In: Inventive Systems and Control: Proceedings of ICISC 2022. Singapore: Springer Nature Singapore. pp. 385\u2013404","DOI":"10.1007\/978-981-19-1012-8_26"}],"container-title":["Innovations in Systems and Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11334-023-00547-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11334-023-00547-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11334-023-00547-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T03:05:23Z","timestamp":1750302323000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11334-023-00547-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,28]]},"references-count":16,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["547"],"URL":"https:\/\/doi.org\/10.1007\/s11334-023-00547-w","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-3084068\/v1","asserted-by":"object"}]},"ISSN":["1614-5046","1614-5054"],"issn-type":[{"value":"1614-5046","type":"print"},{"value":"1614-5054","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,28]]},"assertion":[{"value":"19 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 December 2023","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 competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}