{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T03:01:35Z","timestamp":1768273295272,"version":"3.49.0"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"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":["Soft Comput"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s00500-025-10482-6","type":"journal-article","created":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T03:06:26Z","timestamp":1742958386000},"page":"2399-2415","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Channel boosted convolutional neural network with segnet based segmentation for an automatic prediction of thyroid cancer"],"prefix":"10.1007","volume":"29","author":[{"given":"Leelavathi","family":"Arepalli","sequence":"first","affiliation":[]},{"given":"Venkata Rao","family":"Kasukiurthi","sequence":"additional","affiliation":[]},{"given":"Madhavi","family":"Dabbiru","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,24]]},"reference":[{"key":"10482_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103871","volume":"122","author":"F Abdolali","year":"2020","unstructured":"Abdolali F, Kapur J, Jaremko JL, Noga M, Hareendranathan AR, Punithakumar K (2020) Automated thyroid nodule detection from ultrasound imaging using deep convolutional neural networks. Comput Biol Med 122:103871","journal-title":"Comput Biol Med"},{"key":"10482_CR3","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/9809932","author":"T Alyas","year":"2022","unstructured":"Alyas T, Hamid M, Alissa K, Faiz T, Tabassum N, Ahmad A (2022) Empirical method for thyroid disease classification using a machine learning approach. BioMed Res Int. https:\/\/doi.org\/10.1155\/2022\/9809932","journal-title":"BioMed Res Int"},{"issue":"15","key":"10482_CR14","doi-asserted-by":"publisher","first-page":"24802","DOI":"10.1109\/JSEN.2024.3399008","volume":"24","author":"N Bhavana","year":"2024","unstructured":"Bhavana N, Kodabagi M M, Muthu Kumar B, Ajay P, Muthukumaran N, Ahilan A (2024) POT-YOLO: Real-Time Road Potholes Detection using Edge Segmentation based Yolo V8 Network. IEEE Sens J 24(15):24802\u201324809","journal-title":"IEEE Sens J"},{"key":"10482_CR5","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1007\/s12020-020-02418-x","volume":"70","author":"A Campenn\u00ec","year":"2020","unstructured":"Campenn\u00ec A, Barbaro D, Guzzo M, Capoccetti F, Giovanella L (2020) Personalized management of differentiated thyroid cancer in real life\u2013practical guidance from a multidisciplinary panel of experts. Endocrine 70:280\u2013291","journal-title":"Endocrine"},{"key":"10482_CR6","doi-asserted-by":"publisher","first-page":"4635","DOI":"10.1007\/s00330-019-06036-8","volume":"29","author":"SJ Cho","year":"2019","unstructured":"Cho SJ, Suh CH, Baek JH, Chung SR, Choi YJ, Lee JH (2019) Diagnostic performance of CT in detection of metastatic cervical lymph nodes in patients with thyroid cancer: a systematic review and meta-analysis. Eur Radiol 29:4635\u20134647","journal-title":"Eur Radiol"},{"key":"10482_CR7","doi-asserted-by":"publisher","unstructured":"Dash S, Parida P & Mohanty JR (2024) Illumination robust deep convolutional neural network for medical image classification. Soft Comput 28:461. https:\/\/doi.org\/10.1007\/s00500-023-07918-2","DOI":"10.1007\/s00500-023-07918-2"},{"key":"10482_CR8","unstructured":"Dataset1: https:\/\/www.acr.org\/Clinical-Resources\/Reporting-and-Data-Systems\/TI-RADS"},{"issue":"2","key":"10482_CR9","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1530\/EJE-20-0080","volume":"183","author":"M de Ridder","year":"2020","unstructured":"de Ridder M, van Dijkum EN, Engelsman A, Kapiteijn E, Kl\u00fcmpen HJ, Rasch CR (2020) Anaplastic thyroid carcinoma: a nationwide cohort study on incidence, treatment and survival in the Netherlands over 3 decades. Eur J Endocrinol 183(2):203\u2013209","journal-title":"Eur J Endocrinol"},{"key":"10482_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106172","volume":"150","author":"P Deng","year":"2022","unstructured":"Deng P, Han X, Wei X, Chang L (2022) Automatic classification of thyroid nodules in ultrasound images using a multi-task attention network guided by clinical knowledge. Comput Biol Med 150:106172","journal-title":"Comput Biol Med"},{"issue":"5","key":"10482_CR11","doi-asserted-by":"publisher","first-page":"345","DOI":"10.5144\/0256-4947.2019.345","volume":"39","author":"A Doubi","year":"2019","unstructured":"Doubi A, Al-Qannass A, Al-Angari SS, Al-Qahtani KH, Alessa M, Al-Dhahri S (2019) Trends in thyroid carcinoma among thyroidectomy patients: a 12-year multicenter study. Ann Saudi Med 39(5):345\u2013349","journal-title":"Ann Saudi Med"},{"issue":"3","key":"10482_CR12","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1016\/j.ultrasmedbio.2020.11.024","volume":"47","author":"H Fang","year":"2021","unstructured":"Fang H, Gong L, Xu Y, Zhuo Y, Kong W, Peng C, Yuan J (2021) Reliable thyroid carcinoma detection with real-time intelligent analysis of ultrasound images. Ultrasound Med Biol 47(3):590\u2013602","journal-title":"Ultrasound Med Biol"},{"key":"10482_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.jddst.2020.102221","volume":"61","author":"E Ghazy","year":"2021","unstructured":"Ghazy E, Kumar A, Barani M, Kaur I, Rahdar A, Behl T (2021) Scrutinizing the therapeutic and diagnostic potential of nanotechnology in thyroid cancer: edifying drug targeting by nano-oncotherapeutics. J Drug Deliver Sci Technol 61:102221","journal-title":"J Drug Deliver Sci Technol"},{"key":"10482_CR15","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/8710862","author":"Z Hu","year":"2018","unstructured":"Hu Z, Yang B, Li T, Li J (2018) Thyroid cancer detection by ultrasound molecular imaging with SHP2-targeted perfluorocarbon nanoparticles. Contrast Media Mol Imaging. https:\/\/doi.org\/10.1155\/2018\/8710862","journal-title":"Contrast Media Mol Imaging"},{"issue":"3","key":"10482_CR16","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1089\/thy.2018.0509","volume":"29","author":"T Ibrahimpasic","year":"2019","unstructured":"Ibrahimpasic T, Ghossein R, Shah JP, Ganly I (2019) Poorly differentiated carcinoma of the thyroid gland: current status and future prospects. Thyroid 29(3):311\u2013321","journal-title":"Thyroid"},{"key":"10482_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbo.2020.100282","volume":"21","author":"NM I\u00f1iguez-Ariza","year":"2020","unstructured":"I\u00f1iguez-Ariza NM, Bible KC, Clarke BL (2020) Bone metastases in thyroid cancer. J Bone Oncol 21:100282","journal-title":"J Bone Oncol"},{"key":"10482_CR4","doi-asserted-by":"publisher","first-page":"111837","DOI":"10.1016\/j.asoc.2024.111837","volume":"162","author":"S Karthick","year":"2024","unstructured":"Karthick S, Muthukumaran N (2024) Deep RegNet-150 architecture for single image super resolution of real-time unpaired image data. Appl Soft Comput 162:111837. https:\/\/doi.org\/10.1016\/j.asoc.2024.111837","journal-title":"Appl Soft Comput"},{"key":"10482_CR18","unstructured":"Khan A, Sohail A, Ali A (2018) A new channel boosted convolutional neural network using transfer learning. arXiv preprint arXiv:1804.08528."},{"issue":"1","key":"10482_CR19","doi-asserted-by":"publisher","first-page":"18745","DOI":"10.1038\/s41598-019-55370-w","volume":"9","author":"H Kim","year":"2019","unstructured":"Kim H, Park SY, Jung J, Kim JH, Hahn SY, Shin JH, Oh YL, Chung MK, Kim HI, Kim SW, Chung JH (2019) Improved survival after early detection of asymptomatic distant metastasis in patients with thyroid cancer. Sci Rep 9(1):18745","journal-title":"Sci Rep"},{"key":"10482_CR20","doi-asserted-by":"publisher","first-page":"63482","DOI":"10.1109\/ACCESS.2020.2982390","volume":"8","author":"V Kumar","year":"2020","unstructured":"Kumar V, Webb J, Gregory A, Meixner DD, Knudsen JM, Callstrom M, Fatemi M, Alizad A (2020) Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. IEEE Access 8:63482\u201363496","journal-title":"IEEE Access"},{"issue":"15","key":"10482_CR21","doi-asserted-by":"publisher","first-page":"3891","DOI":"10.3390\/cancers13153891","volume":"13","author":"YJ Lin","year":"2021","unstructured":"Lin YJ, Chao TK, Khalil MA, Lee YC, Hong DZ, Wu JJ, Wang CW (2021) Deep learning fast screening approach on cytological whole slides for thyroid cancer diagnosis. Cancers 13(15):3891","journal-title":"Cancers"},{"key":"10482_CR22","doi-asserted-by":"crossref","unstructured":"Liu T, Xie S, Yu J, Niu L, Sun W (2017) Classification of thyroid nodules in ultrasound images using deep model based transfer learning and hybrid features. In: 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP) 919\u2013923. IEEE","DOI":"10.1109\/ICASSP.2017.7952290"},{"key":"10482_CR23","doi-asserted-by":"publisher","first-page":"1895","DOI":"10.1007\/s11548-017-1649-7","volume":"12","author":"J Ma","year":"2017","unstructured":"Ma J, Wu F, Jiang TA, Zhao Q, Kong D (2017) Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks. Int J Comput Assist Radiol Surg 12:1895\u20131910","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"10482_CR2","doi-asserted-by":"publisher","first-page":"34913","DOI":"10.1007\/s11042-023-16739-2","volume":"83","author":"M Madanan","year":"2024","unstructured":"Madanan M, Muthukumaran N, Tiwari S, Vijay A, Saha I (2024) RSA based improved YOLOv3 network for segmentation and detection of weed species. Multimed Tools Appl 83:34913\u201334942","journal-title":"Multimed Tools Appl"},{"key":"10482_CR24","doi-asserted-by":"crossref","unstructured":"Patel S, Bharath KP, Balaji S, Muthu RK (2020) Comparative study on histogram equalization techniques for medical image enhancement. In: Soft Computing for Problem Solving: SocProS 2018, 1:657\u2013669. Springer Singapore","DOI":"10.1007\/978-981-15-0035-0_54"},{"key":"10482_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.canep.2019.101664","volume":"64","author":"SJ Schonfeld","year":"2020","unstructured":"Schonfeld SJ, Morton LM, de Gonz\u00e1lez AB, Curtis RE, Kitahara CM (2020) Risk of second primary papillary thyroid cancer among adult cancer survivors in the United States, 2000\u20132015. Cancer Epidemiol 64:101664","journal-title":"Cancer Epidemiol"},{"issue":"6","key":"10482_CR26","doi-asserted-by":"publisher","first-page":"527","DOI":"10.3390\/medicina57060527","volume":"57","author":"VV Vadhiraj","year":"2021","unstructured":"Vadhiraj VV, Simpkin A, O\u2019Connell J, Singh Ospina N, Maraka S, O\u2019Keeffe DT (2021) Ultrasound image classification of thyroid nodules using machine learning techniques. Medicina 57(6):527","journal-title":"Medicina"},{"key":"10482_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101665","volume":"61","author":"L Wang","year":"2020","unstructured":"Wang L, Zhang L, Zhu M, Qi X, Yi Z (2020a) Automatic diagnosis for thyroid nodules in ultrasound images by deep neural networks. Med Image Anal 61:101665","journal-title":"Med Image Anal"},{"key":"10482_CR28","doi-asserted-by":"publisher","DOI":"10.5604\/01.3001.0013.5856","author":"X Wang","year":"2020","unstructured":"Wang X, Hou RM, Gao XY, Xin BJ (2020b) Research on yarn diameter and unevenness based on an adaptive median filter denoising algorithm. Fibres Textiles Eastern Eur. https:\/\/doi.org\/10.5604\/01.3001.0013.5856","journal-title":"Fibres Textiles Eastern Eur"},{"key":"10482_CR29","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-021-08298-7","author":"J Wang","year":"2022","unstructured":"Wang J, Jiang J, Zhang D, Zhang YZ, Guo L, Jiang Y et al (2022) An integrated AI model to improve diagnostic accuracy of ultrasound and output known risk features in suspicious thyroid nodules. Eur Radiol. https:\/\/doi.org\/10.1007\/s00330-021-08298-7","journal-title":"Eur Radiol"},{"issue":"4","key":"10482_CR30","doi-asserted-by":"publisher","first-page":"256","DOI":"10.3390\/ijgi9040256","volume":"9","author":"L Weng","year":"2020","unstructured":"Weng L, Xu Y, Xia M, Zhang Y, Liu J, Xu Y (2020) Water areas segmentation from remote sensing images using a separable residual segnet network. ISPRS Int J Geo Inf 9(4):256","journal-title":"ISPRS Int J Geo Inf"},{"key":"10482_CR31","doi-asserted-by":"publisher","first-page":"27917","DOI":"10.1109\/ACCESS.2022.3156096","volume":"10","author":"X Zhao","year":"2022","unstructured":"Zhao X, Shen X, Wan W, Lu Y, Hu S, Xiao R et al (2022) Automatic thyroid ultrasound image classification using feature fusion network. IEEE Access 10:27917\u201327924","journal-title":"IEEE Access"},{"key":"10482_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultras.2020.106300","volume":"110","author":"YC Zhu","year":"2021","unstructured":"Zhu YC, AlZoubi A, Jassim S, Jiang Q, Zhang Y, Wang YB, Ye XD, Hongbo DU (2021) A generic deep learning framework to classify thyroid and breast lesions in ultrasound images. Ultrasonics 110:106300","journal-title":"Ultrasonics"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-025-10482-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-025-10482-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-025-10482-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,18]],"date-time":"2025-04-18T10:32:44Z","timestamp":1744972364000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-025-10482-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3]]},"references-count":32,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["10482"],"URL":"https:\/\/doi.org\/10.1007\/s00500-025-10482-6","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3]]},"assertion":[{"value":"8 November 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 March 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article is a completely original work of its authors; it has not been published before and will not be sent to other publications until the journal\u2019s editorial board decides not to accept it for publication.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}}]}}