{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:00:50Z","timestamp":1772906450769,"version":"3.50.1"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T00:00:00Z","timestamp":1696291200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T00:00:00Z","timestamp":1696291200000},"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\/s11042-023-16980-9","type":"journal-article","created":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T07:02:02Z","timestamp":1696316522000},"page":"38083-38108","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Medical image segmentation using an optimized three-tier quantum convolutional neural network trained with hybrid optimization approach"],"prefix":"10.1007","volume":"83","author":[{"given":"S. V. S","family":"Prasad","sequence":"first","affiliation":[]},{"given":"B. Chinna","family":"Rao","sequence":"additional","affiliation":[]},{"given":"M. Koteswara","family":"Rao","sequence":"additional","affiliation":[]},{"given":"K. Ravi","family":"Kumar","sequence":"additional","affiliation":[]},{"given":"Srisailapu D.","family":"Vara Prasad","sequence":"additional","affiliation":[]},{"given":"Chappa","family":"Ramesh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,3]]},"reference":[{"issue":"10","key":"16980_CR1","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","volume":"38","author":"Z Gu","year":"2019","unstructured":"Gu Z, Cheng J, Fu H, Zhou K, Hao H, Zhao Y, Zhang T, Gao S, Liu J (2019) Ce-net: Context encoder network for 2d medical image segmentation. IEEE Trans Med Imaging 38(10):2281\u20132292","journal-title":"IEEE Trans Med Imaging"},{"key":"16980_CR2","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1007\/s10278-019-00227-x","volume":"32","author":"MH Hesamian","year":"2019","unstructured":"Hesamian MH, Jia W, He X, Kennedy P (2019) Deep learning techniques for medical image segmentation: achievements and challenges. J Digit Imaging 32:582\u2013596","journal-title":"J Digit Imaging"},{"key":"16980_CR3","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.cogsys.2018.03.005","volume":"50","author":"M Vardhana","year":"2018","unstructured":"Vardhana M, Arunkumar N, Lasrado S, Abdulhay E, Ramirez-Gonzalez G (2018) Convolutional neural network for bio-medical image segmentation with hardware acceleration. Cogn Syst Res 50:10\u201314","journal-title":"Cogn Syst Res"},{"key":"16980_CR4","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.compbiomed.2018.10.012","volume":"103","author":"J Minnema","year":"2018","unstructured":"Minnema J, van Eijnatten M, Kouw W, Diblen F, Mendrik A, Wolff J (2018) CT image segmentation of bone for medical additive manufacturing using a convolutional neural network. Comput Biol Med 103:130\u2013139","journal-title":"Comput Biol Med"},{"key":"16980_CR5","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1007\/s40684-020-00197-4","volume":"8","author":"TP Nguyen","year":"2021","unstructured":"Nguyen TP, Choi S, Park SJ, Park SH, Yoon J (2021) Inspecting method for defective casting products with convolutional neural network (CNN). Intl J Precision Eng Manuf Green Technol 8:583\u2013594","journal-title":"Intl J Precision Eng Manuf Green Technol"},{"issue":"15-16","key":"16980_CR6","doi-asserted-by":"publisher","first-page":"10233","DOI":"10.1007\/s11042-019-7419-5","volume":"79","author":"K Sekaran","year":"2020","unstructured":"Sekaran K, Chandana P, Krishna NM, Kadry S (2020) Deep learning convolutional neural network (CNN) With Gaussian mixture model for predicting pancreatic cancer. Multimed Tools Appl 79(15-16):10233\u201310247","journal-title":"Multimed Tools Appl"},{"key":"16980_CR7","doi-asserted-by":"publisher","first-page":"106062","DOI":"10.1016\/j.knosys.2020.106062","volume":"201","author":"F Sultana","year":"2020","unstructured":"Sultana F, Sufian A, Dutta P (2020) Evolution of image segmentation using deep convolutional neural network: A survey. Knowl-Based Syst 201:106062","journal-title":"Knowl-Based Syst"},{"key":"16980_CR8","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.neucom.2019.01.110","volume":"392","author":"M Baldeon-Calisto","year":"2020","unstructured":"Baldeon-Calisto M, Lai-Yuen SK (2020) AdaResU-Net: Multiobjective adaptive convolutional neural network for medical image segmentation. Neurocomputing 392:325\u2013340","journal-title":"Neurocomputing"},{"key":"16980_CR9","doi-asserted-by":"publisher","first-page":"12035","DOI":"10.1007\/s11042-020-10053-x","volume":"80","author":"S Sharma","year":"2021","unstructured":"Sharma S, Saha AK, Majumder A, Nama S (2021) MPBOA-A novel hybrid butterfly optimization algorithm with symbiosis organisms search for global optimization and image segmentation. Multimed Tools Appl 80:12035\u201312076","journal-title":"Multimed Tools Appl"},{"key":"16980_CR10","doi-asserted-by":"publisher","first-page":"2033","DOI":"10.1007\/s00500-017-2916-9","volume":"23","author":"M Zhang","year":"2019","unstructured":"Zhang M, Jiang W, Zhou X, Xue Y, Chen S (2019) A hybrid biogeography-based optimization and fuzzy C-means algorithm for image segmentation. Soft Comput 23:2033\u20132046","journal-title":"Soft Comput"},{"key":"16980_CR11","doi-asserted-by":"publisher","first-page":"102035","DOI":"10.1016\/j.media.2021.102035","volume":"71","author":"J Ma","year":"2021","unstructured":"Ma J, Chen J, Ng M, Huang R, Li Y, Li C, Yang X, Martel AL (2021) Loss odyssey in medical image segmentation. Med Image Anal 71:102035","journal-title":"Med Image Anal"},{"key":"16980_CR12","doi-asserted-by":"publisher","first-page":"104105","DOI":"10.1016\/j.engappai.2020.104105","volume":"98","author":"M Abd Elaziz","year":"2021","unstructured":"Abd Elaziz M, Yousri D, Al-qaness MA, AbdelAty AM, Radwan AG, Ewees AA (2021) A Grunwald\u2013Letnikov based Manta ray foraging optimizer for global optimization and image segmentation. Eng Appl Artif Intell 98:104105","journal-title":"Eng Appl Artif Intell"},{"key":"16980_CR13","doi-asserted-by":"publisher","first-page":"23003","DOI":"10.1007\/s11042-019-7515-6","volume":"78","author":"M Ahmadi","year":"2019","unstructured":"Ahmadi M, Kazemi K, Aarabi A, Niknam T, Helfroush MS (2019) Image segmentation using multilevel thresholding based on modified bird mating optimization. Multimed Tools Appl 78:23003\u201323027","journal-title":"Multimed Tools Appl"},{"key":"16980_CR14","doi-asserted-by":"publisher","first-page":"9221","DOI":"10.1007\/s13369-019-03874-y","volume":"44","author":"X Yue","year":"2019","unstructured":"Yue X, Zhang H (2019) Improved hybrid bat algorithm with invasive weed and its application in image segmentation. Arab J Sci Eng 44:9221\u20139234","journal-title":"Arab J Sci Eng"},{"issue":"27-28","key":"16980_CR15","doi-asserted-by":"publisher","first-page":"19075","DOI":"10.1007\/s11042-019-08138-3","volume":"79","author":"M Chouksey","year":"2020","unstructured":"Chouksey M, Jha RK, Sharma R (2020) A fast technique for image segmentation based on two meta-heuristic algorithms. Multimed Tools Appl 79(27-28):19075\u201319127","journal-title":"Multimed Tools Appl"},{"issue":"11","key":"16980_CR16","doi-asserted-by":"publisher","first-page":"2453","DOI":"10.1109\/TMI.2018.2835303","volume":"37","author":"L Chen","year":"2018","unstructured":"Chen L, Bentley P, Mori K, Misawa K, Fujiwara M, Rueckert D (2018) DRINet for medical image segmentation. IEEE Trans Med Imaging 37(11):2453\u20132462","journal-title":"IEEE Trans Med Imaging"},{"issue":"7","key":"16980_CR17","doi-asserted-by":"publisher","first-page":"1562","DOI":"10.1109\/TMI.2018.2791721","volume":"37","author":"G Wang","year":"2018","unstructured":"Wang G, Li W, Zuluaga MA, Pratt R, Patel PA, Aertsen M, Doel T, David AL, Deprest J, Ourselin S, Vercauteren T (2018) Interactive medical image segmentation using deep learning with image-specific fine tuning. IEEE Trans Med Imaging 37(7):1562\u20131573","journal-title":"IEEE Trans Med Imaging"},{"key":"16980_CR18","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.1007\/s00521-017-3158-6","volume":"29","author":"F Jiang","year":"2018","unstructured":"Jiang F, Grigorev A, Rho S, Tian Z, Fu Y, Jifara W, Adil K, Liu S (2018) Medical image semantic segmentation based on deep learning. Neural Comput & Applic 29:1257\u20131265","journal-title":"Neural Comput & Applic"},{"issue":"11","key":"16980_CR19","doi-asserted-by":"publisher","first-page":"2642","DOI":"10.1109\/TMI.2019.2907805","volume":"38","author":"J Sourati","year":"2019","unstructured":"Sourati J, Gholipour A, Dy JG, Tomas-Fernandez X, Kurugol S, Warfield SK (2019) Intelligent labeling based on fisher information for medical image segmentation using deep learning. IEEE Trans Med Imaging 38(11):2642\u20132653","journal-title":"IEEE Trans Med Imaging"},{"issue":"2","key":"16980_CR20","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1109\/TMI.2019.2930068","volume":"39","author":"D Karimi","year":"2019","unstructured":"Karimi D, Salcudean SE (2019) Reducing the hausdorff distance in medical image segmentation with convolutional neural networks. IEEE Trans Med Imaging 39(2):499\u2013513","journal-title":"IEEE Trans Med Imaging"},{"key":"16980_CR21","doi-asserted-by":"publisher","first-page":"101589","DOI":"10.1016\/j.bspc.2019.101589","volume":"53","author":"A Feng-Ping","year":"2019","unstructured":"Feng-Ping A, Zhi-Wen L (2019) Medical image segmentation algorithm based on feedback mechanism convolutional neural network. Biomed Signal Process Control 53:101589","journal-title":"Biomed Signal Process Control"},{"issue":"2","key":"16980_CR22","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1109\/TMI.2020.3035253","volume":"40","author":"R Gu","year":"2020","unstructured":"Gu R, Wang G, Song T, Huang R, Aertsen M, Deprest J, Ourselin S, Vercauteren T, Zhang S (2020) CA-Net: Comprehensive attention convolutional neural networks for explainable medical image segmentation. IEEE Trans Med Imaging 40(2):699\u2013711","journal-title":"IEEE Trans Med Imaging"},{"issue":"10","key":"16980_CR23","doi-asserted-by":"publisher","first-page":"3008","DOI":"10.1109\/TMI.2020.2983721","volume":"39","author":"S Feng","year":"2020","unstructured":"Feng S, Zhao H, Shi F, Cheng X, Wang M, Ma Y, Xiang D, Zhu W, Chen X (2020) CPFNet: Context pyramid fusion network for medical image segmentation. IEEE Trans Med Imaging 39(10):3008\u20133018","journal-title":"IEEE Trans Med Imaging"},{"key":"16980_CR24","doi-asserted-by":"publisher","first-page":"106230","DOI":"10.1016\/j.cmpb.2021.106230","volume":"208","author":"H Ma","year":"2021","unstructured":"Ma H, Zou Y, Liu PX (2021) MHSU-Net: A more versatile neural network for medical image segmentation. Comput Methods Prog Biomed 208:106230","journal-title":"Comput Methods Prog Biomed"},{"issue":"4","key":"16980_CR25","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1007\/s12530-021-09392-3","volume":"13","author":"Q Shi","year":"2022","unstructured":"Shi Q, Yin S, Wang K, Teng L, Li H (2022) Multichannel convolutional neural network-based fuzzy active contour model for medical image segmentation. Evol Syst 13(4):535\u2013549","journal-title":"Evol Syst"},{"key":"16980_CR26","doi-asserted-by":"crossref","unstructured":"Cuevas E, Reyna-Orta A (2014) A cuckoo search algorithm for multimodal optimization. Sci World J, 2014","DOI":"10.1155\/2014\/497514"},{"issue":"7","key":"16980_CR27","doi-asserted-by":"publisher","first-page":"3330","DOI":"10.1109\/TCYB.2019.2894498","volume":"50","author":"L Wang","year":"2019","unstructured":"Wang L, Qian X, Zhang Y, Shen J, Cao X (2019) Enhancing sketch-based image retrieval by cnn semantic re-ranking. IEEE Trans Cybern 50(7):3330\u20133342","journal-title":"IEEE Trans Cybern"},{"key":"16980_CR28","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1016\/j.ins.2020.11.026","volume":"569","author":"J Shen","year":"2021","unstructured":"Shen J, Robertson N (2021) BBAS: Towards large scale effective ensemble adversarial attacks against deep neural network learning. Inf Sci 569:469\u2013478","journal-title":"Inf Sci"},{"key":"16980_CR29","doi-asserted-by":"crossref","unstructured":"Dwivedi N, Singh DK, Kushwaha DS (2023) A novel approach for suspicious activity detection with deep learning. Multimed Tools Appl, 1-24","DOI":"10.1007\/s11042-023-14445-7"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16980-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-16980-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16980-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,3]],"date-time":"2024-04-03T10:29:07Z","timestamp":1712140147000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-16980-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,3]]},"references-count":29,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["16980"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-16980-9","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,3]]},"assertion":[{"value":"18 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 August 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 September 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 October 2023","order":4,"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 that we have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}