{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T21:11:31Z","timestamp":1780348291222,"version":"3.54.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T00:00:00Z","timestamp":1708560000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T00:00:00Z","timestamp":1708560000000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-18521-4","type":"journal-article","created":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T11:06:19Z","timestamp":1708599979000},"page":"46283-46323","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["GIEnsemformerCADx: A hybrid ensemble learning approach for enhanced gastrointestinal cancer recognition"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3237-2859","authenticated-orcid":false,"given":"Akella S. Narasimha","family":"Raju","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2895-0839","authenticated-orcid":false,"given":"K.","family":"Venkatesh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5327-9795","authenticated-orcid":false,"given":"B.","family":"Padmaja","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7861-2451","authenticated-orcid":false,"given":"G. Sucharitha","family":"Reddy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,22]]},"reference":[{"key":"18521_CR1","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1200\/GO.20.00122","volume":"6","author":"P Mathur","year":"2020","unstructured":"Mathur P, Sathishkumar K, Chaturvedi M, Das P, Sudarshan KL, Santhappan S (2020) Cancer Statistics, 2020: Report From National Cancer Registry Programme, India. JCO Global Oncol-An Am Soc Clin Oncol J 6:1063\u20131075","journal-title":"JCO Global Oncol-An Am Soc Clin Oncol J"},{"key":"18521_CR2","unstructured":"\"Americal cancer Society,\" ACS, [Online]. Available: https:\/\/www.cancer.org\/cancer\/colon-rectal-cancer\/. Accessed 15 Nov 2022"},{"key":"18521_CR3","unstructured":"American Cancer Society (2020) Colorectal cancer facts & figures 2020-2022. American Cancer Society, Atlanta, GA"},{"issue":"64","key":"18521_CR4","first-page":"25","volume":"6","author":"SE Kudo","year":"2021","unstructured":"Kudo SE, Mori Y, Abdel-Aal UM, Misawa M, Itoh H, Oda M, Mori K (2021) Artificial intelligence and computer-aided diagnosis for colonoscopy: where do we stand now? Trans Gastroenterol Hepatol 6(64):25","journal-title":"Trans Gastroenterol Hepatol"},{"issue":"3","key":"18521_CR5","doi-asserted-by":"publisher","first-page":"1581","DOI":"10.3390\/curroncol28030149","volume":"28","author":"A Mitsala","year":"2021","unstructured":"Mitsala A, Tsalikidis C, Pitiakoudis M, Simopoulos C, Tsaroucha AK (2021) Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era. Curr Oncol 28(3):1581\u20131607","journal-title":"Curr Oncol"},{"issue":"15","key":"18521_CR6","doi-asserted-by":"publisher","first-page":"5040","DOI":"10.3390\/app10155040","volume":"10","author":"R Fonoll\u00e0","year":"2020","unstructured":"Fonoll\u00e0 R, van der Zander EWQ, Schreuder RM, Masclee AA, Schoon EJ, van der Sommen F, de With PH (2020) A CNN CADx System for Multimodal Classification of Colorectal Polyps Combining WL, BLI, and LCI Modalities. Appl Sci 10(15):5040","journal-title":"Appl Sci"},{"issue":"1","key":"18521_CR7","first-page":"738","volume":"12","author":"AS Raju","year":"2022","unstructured":"Raju AS, Jayavel K, Rajalakshmi T (2022) Intelligent recognition of colorectal cancer combining application of computer-assisted diagnosis with deep learning approaches. Int J Elect Comput Eng 12(1):738\u2013747","journal-title":"Int J Elect Comput Eng"},{"issue":"2","key":"18521_CR8","doi-asserted-by":"publisher","first-page":"321","DOI":"10.3390\/cancers13020321","volume":"13","author":"YK Wang","year":"2021","unstructured":"Wang YK, Syu HY, Chen YH, Chung CS, Tseng YS, Ho SY, Huang CW, Wu IC, Wang HC (2021) Endoscopic Images by a Single-Shot Multibox Detector for the Identification of Early Cancerous Lesions in the Esophagus: A Pilot Study. Cancers 13(2):321","journal-title":"Cancers"},{"key":"18521_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2020.3015607","volume":"70","author":"D Banik","year":"2020","unstructured":"Banik D, Roy K, Bhattacharjee D, Nasipuri M, Krejcar O (2020) Polyp-Net: A Multimodel Fusion Network for Polyp Segmentation. IEEE Trans Instrum Meas 70:1\u201312","journal-title":"IEEE Trans Instrum Meas"},{"key":"18521_CR10","doi-asserted-by":"publisher","first-page":"103638","DOI":"10.1016\/j.jbi.2020.103638","volume":"113","author":"\u015e \u00d6zt\u00fcrk","year":"2021","unstructured":"\u00d6zt\u00fcrk \u015e, \u00d6zkaya U (2021) Residual LSTM layered CNN for classification of gastrointestinal tract diseases. J Biomed Inform 113:103638","journal-title":"J Biomed Inform"},{"issue":"3","key":"18521_CR11","doi-asserted-by":"publisher","first-page":"690","DOI":"10.1109\/TMI.2021.3123567","volume":"41","author":"Y Meng","year":"2022","unstructured":"Meng Y, Zhang H, Zhao Y, Yang X, Qiao Y, MacCormick IJ, Huang X, Zheng Y (2022) Graph-Based Region and Boundary Aggregation for Biomedical Image Segmentation. IEEE Trans On Med Imaging 41(3):690\u2013701","journal-title":"IEEE Trans On Med Imaging"},{"issue":"2","key":"18521_CR12","first-page":"616","volume":"9","author":"KB Park","year":"2022","unstructured":"Park KB, Lee JY (2022) SwinE-Net: hybrid deep learning approach to novel polyp segmentation using convolutional neural network and Swin Transformer. J Comput Des Eng 9(2):616\u2013632","journal-title":"J Comput Des Eng"},{"key":"18521_CR13","doi-asserted-by":"publisher","first-page":"80575","DOI":"10.1109\/ACCESS.2022.3195241","volume":"10","author":"NT Duc","year":"2022","unstructured":"Duc NT, Oanh NT, Thuy NT, Triet TM, Dinh VS (2022) ColonFormer: An Efficient Transformer based Method for Colon Polyp Segmentation. IEEE Access 10:80575\u201380586","journal-title":"IEEE Access"},{"key":"18521_CR14","doi-asserted-by":"publisher","unstructured":"Li K, Fathan MI, Patel K, Zhang T, Zhong C, Bansal A et al (2021) Colonoscopy polyp detection and classification: dataset creation and comparative evaluations. PLoS ONE 16(8):e0255809. https:\/\/doi.org\/10.1371\/journal.pone.0255809","DOI":"10.1371\/journal.pone.0255809"},{"key":"18521_CR15","doi-asserted-by":"crossref","unstructured":"Akella S. Narasimha Raju, Kayalvizhi Jayavel, T. Rajalakshmi, \"ColoRectalCADx: Expeditious Recognition of Colorectal Cancer with Integrated Convolutional Neural Networks and Visual Explanations Using Mixed Dataset Evidence,\" Computational and Mathematical Methods in Medicine, 10 November 2022.","DOI":"10.1155\/2022\/8723957"},{"key":"18521_CR16","unstructured":"(2015) [Online]. https:\/\/www.kaggle.com\/balraj98\/cvcclinicdb. Accessed 25 May 2021"},{"key":"18521_CR17","unstructured":"(2016) [Online]. https:\/\/datasets.simula.no\/kvasir\/. Accessed 3 July 2021"},{"key":"18521_CR18","unstructured":"(2020) [Online]. https:\/\/datasets.simula.no\/hyper-kvasir\/. Accessed 3 July 2021"},{"issue":"3","key":"18521_CR19","doi-asserted-by":"publisher","first-page":"1641","DOI":"10.1109\/JIOT.2019.2946296","volume":"7","author":"R Cao","year":"2020","unstructured":"Cao R, Tang Z, Liu C, Veeravalli B (2020) A Scalable Multicloud Storage Architecture for Cloud-Supported Medical Internet of Things. IEE Int Things J 7(3):1641\u20131654","journal-title":"IEE Int Things J"},{"issue":"5","key":"18521_CR20","doi-asserted-by":"publisher","first-page":"141","DOI":"10.3390\/jimaging8050141","volume":"8","author":"P Oza","year":"2022","unstructured":"Oza P, Sharma P, Patel S, Adedoyin F, Bruno A (2022) A Image Augmentation Techniques for Mammogram Analysis. J Imaging 8(5):141","journal-title":"J Imaging"},{"key":"18521_CR21","doi-asserted-by":"publisher","unstructured":"Chen CF, Fan Q, Panda R (2021) CrossViT: cross-attention multi-scale vision transformer for image classification. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV). IEEE, Montreal, QC, Canada, pp 347\u2013356. https:\/\/doi.org\/10.1109\/ICCV48922.2021.00041","DOI":"10.1109\/ICCV48922.2021.00041"},{"key":"18521_CR22","unstructured":"\"https:\/\/towardsdatascience.com\/,\" towards datascience, 5 October 2022. [Online]. Available: https:\/\/towardsdatascience.com\/using-transformers-for-computer-vision-6f764c5a078b. [Accessed 5 June 2023]."},{"key":"18521_CR23","doi-asserted-by":"publisher","first-page":"102654","DOI":"10.1016\/j.bspc.2021.102654","volume":"68","author":"T Rahim","year":"2021","unstructured":"Rahim T, Hassan SA, Shin SY (2021) A deep convolutional neural network for the detection of polyps in colonoscopy images. Biomed Signal Process Control 68:102654","journal-title":"Biomed Signal Process Control"},{"key":"18521_CR24","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1155\/2022\/4325412","volume":"2022","author":"AS Narasimha Raju","year":"2022","unstructured":"Narasimha Raju AS, Jayavel K, Rajalakshmi T (2022) Dexterous Identification of Carcinoma through ColoRectalCADx with Dichotomous Fusion CNN and UNet Semantic Segmentation. Comput Intell Neurosci 2022:29","journal-title":"Comput Intell Neurosci"},{"key":"18521_CR25","doi-asserted-by":"publisher","first-page":"103950","DOI":"10.1016\/j.compbiomed.2020.103950","volume":"124","author":"S Igarashi","year":"2020","unstructured":"Igarashi S, Sasaki Y, Mikami T, Sakuraba H, Fukuda S (2020) Anatomical classification of upper gastrointestinal organs under various image capture conditions using AlexNet. Comput Biol Med 124:103950","journal-title":"Comput Biol Med"},{"key":"18521_CR26","doi-asserted-by":"publisher","unstructured":"Redmon J, Farhadi A (2017) YOLO9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Honolulu, pp 7263\u20137271. https:\/\/doi.org\/10.1109\/CVPR.2017.690","DOI":"10.1109\/CVPR.2017.690"},{"key":"18521_CR27","doi-asserted-by":"publisher","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, pp 2261\u20132269. https:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"18521_CR28","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2016) Inception-v4, inception-ResNet and the impact of residual connections on learning. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. AAAI Press, San Francisco, CA, pp 4278\u20134284","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"18521_CR29","doi-asserted-by":"publisher","first-page":"101838","DOI":"10.1016\/j.compmedimag.2020.101838","volume":"87","author":"S Tripathi","year":"2020","unstructured":"Tripathi S, Singh SK, Lee HK (2020) An end-to-end breast tumour classification model using context-based patch modelling \u2013 A BiLSTM approach for image classification. Comput Med Imaging Graph 87:101838","journal-title":"Comput Med Imaging Graph"},{"key":"18521_CR30","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.jelekin.2018.07.005","volume":"42","author":"H Wu","year":"2018","unstructured":"Wu H, Huang Q, Wang D, Gao L (2018) A CNN-SVM combined model for pattern recognition of knee motion using mechanomyography signals. J Electromyogr Kinesiol 42:136\u2013142","journal-title":"J Electromyogr Kinesiol"},{"key":"18521_CR31","first-page":"1","volume":"2020","author":"W Wu","year":"2020","unstructured":"Wu W, Li D, Du J, Gao X, Gu W, Zhao F, Feng X, Yan H (2020) An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm. Comput Math Methods Med 2020:1\u201310","journal-title":"Comput Math Methods Med"},{"key":"18521_CR32","doi-asserted-by":"publisher","first-page":"e423","DOI":"10.7717\/peerj-cs.423","volume":"7","author":"O Attallah","year":"2021","unstructured":"Attallah O, Sharkas M (2021) GASTRO-CADx: a three stages framework for diagnosing gastrointestinal diseases. PeerJ Comput Sci 7:e423","journal-title":"PeerJ Comput Sci"},{"key":"18521_CR33","doi-asserted-by":"publisher","unstructured":"Liew WS, Tang TB, Lu CK (2022) Computer-aided diagnostic tool for classification of colonic polyp assessment. In: Ibrahim R, Porkumaran K, Kannan R, Mohd Nor N, Prabakar S (eds) International Conference on Artificial Intelligence for Smart Community. Lecture Notes in Electrical Engineering, vol 758. Springer, Singapore. https:\/\/doi.org\/10.1007\/978-981-16-2183-3_71","DOI":"10.1007\/978-981-16-2183-3_71"},{"key":"18521_CR34","doi-asserted-by":"publisher","first-page":"844391","DOI":"10.3389\/fgene.2022.844391","volume":"13","author":"P Sharma","year":"2022","unstructured":"Sharma P, Balabantaray BK, Bora K, Mallik S, Kasugai K, Zhao Z (2022) An Ensemble-Based Deep Convolutional Neural Network for Computer-Aided Polyps Identification From Colonoscopy. Front Gen 13:844391","journal-title":"Front Gen"},{"key":"18521_CR35","doi-asserted-by":"publisher","first-page":"103465","DOI":"10.1016\/j.bspc.2021.103465","volume":"73","author":"JS Nisha","year":"2022","unstructured":"Nisha JS, Gopi VP, Palanisamy P (2022) Automated colorectal polyp detection based on image enhancement and dual-path CNN architecture. Biomed Signal Process Cont 73:103465","journal-title":"Biomed Signal Process Cont"},{"key":"18521_CR36","doi-asserted-by":"publisher","first-page":"20631","DOI":"10.1007\/s00521-023-08859-5","volume":"35","author":"AS Raju","year":"2023","unstructured":"Raju AS, Jayavel K, Rajalakshmi T (2023) An advanced diagnostic ColoRectalCADx utilises CNN and unsupervised visual explanations to discover malignancies. Neural Comput Appl 35:20631\u201320662","journal-title":"Neural Comput Appl"},{"key":"18521_CR37","doi-asserted-by":"publisher","first-page":"738","DOI":"10.3390\/bioengineering10060738","volume":"10","author":"AS Raju","year":"2023","unstructured":"Raju AS, Venkatesh K (2023) EnsemDeepCADx: Empowering Colorectal Cancer Diagnosis with Mixed-Dataset Features and Ensemble Fusion CNNs on Evidence-Based CKHK-22 Dataset. Bioengineering 10:738","journal-title":"Bioengineering"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18521-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-18521-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18521-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T11:09:13Z","timestamp":1714388953000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-18521-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,22]]},"references-count":37,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["18521"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-18521-4","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,22]]},"assertion":[{"value":"5 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 December 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 February 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Although human colonoscopy images are utilised in this work, it is vital to keep in mind that the data used is not collected in real time. The necessary datasets for this investigation may be obtained for free from a wide variety of locations on the internet. Since this study is not collecting data in real time from human participants, the normal ethical clearance procedure for such investigations is unnecessary.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Mr. Akella S. Narasimha Raju spearheaded the procurement of datasets from online sources, conducted meticulous testing, conducted in-depth data analysis, and crafted the initial documentation for this formidable collaboration. Dr. K. Venkatesh, the study's visionary, orchestrated the overall conceptualization, oversaw the intricate analysis of data outcomes, and played a crucial role in the documentation of the extensive research findings in this paper. Dr. B. Padmaja made a significant contribution by formulating the methodology, while Dr. G. Sucharitha Reddy curated the results and subjected them to rigorous analysis. This harmonious synthesis of evolving ideologies and devoted contributions propelled the research to its pinnacle.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Mr. Akella S. Narasimha Raju, Dr. K. Venkatesh, Dr. B. Padmaja, and Dr. G. Sucharitha Reddy state that they have no financial stake in the outcome of this research. No author has any financial or other investment in the results of the research. The primary motivation for this study's existence is to further academic and institutional goals.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}