{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T19:50:52Z","timestamp":1779306652056,"version":"3.51.4"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"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":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s11554-024-01584-9","type":"journal-article","created":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T05:35:10Z","timestamp":1732685710000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Deploying the model of improved heuristic-assisted adaptive SegUnet++ and multi-scale deep learning network for liver tumor segmentation and classification"],"prefix":"10.1007","volume":"22","author":[{"given":"P. Sampurna","family":"Lakshmi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"D.","family":"Nagadevi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K.","family":"Suman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ragodaya","family":"Deepthi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Neetu","family":"Chikyal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,27]]},"reference":[{"key":"1584_CR1","volume":"106","author":"D Zhaoshuo","year":"2023","unstructured":"Zhaoshuo, D., Jiang, H., Zhou, Y.: Leverage prior texture information in deep learning-based liver tumor segmentation: a plug-and-play texture-based auto pseudo label module. Comput. Med. Imaging Graph. 106, 102217 (2023)","journal-title":"Comput. Med. Imaging Graph."},{"issue":"11","key":"1584_CR2","doi-asserted-by":"crossref","first-page":"100304","DOI":"10.1016\/j.modpat.2023.100304","volume":"36","author":"B Raphael","year":"2023","unstructured":"Raphael, B., Rabilloud, N., Perennec, T., P\u00e9cot, T., Garrec, C., Gu\u00e9don, A.F., Delnatte, C., et al.: Deep learning for detecting BRCA mutations in high-grade ovarian cancer based on an innovative tumor segmentation method from whole slide images. Mod. Pathol. 36(11), 100304 (2023)","journal-title":"Mod. Pathol."},{"key":"1584_CR3","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-024-03097-5","author":"S Randar","year":"2024","unstructured":"Randar, S., Shah, V., Kulkarni, H., Suryawanshi, Y., Joshi, A., Sawant, S.: YOLOv8-based frameworks for liver and tumor segmentation task on LiTS. SN Comput. Sci. (2024). https:\/\/doi.org\/10.1007\/s42979-024-03097-5","journal-title":"SN Comput. Sci."},{"key":"1584_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/s44196-024-00542-4","author":"M Reyad","year":"2024","unstructured":"Reyad, M., Sarhan, A.M., Arafa, M.: Architecture optimization for hybrid deep residual networks in liver tumor segmentation using a GA. Int. J. Comput. Intell. Syst. (2024). https:\/\/doi.org\/10.1007\/s44196-024-00542-4","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"1584_CR5","doi-asserted-by":"crossref","first-page":"4189","DOI":"10.3390\/math11194189","volume":"11","author":"F Ullah","year":"2023","unstructured":"Ullah, F., Nadeem, M., Abrar, M., Amin, F., Salam, A., Khan, S.: Enhancing brain tumor segmentation accuracy through scalable federated learning with advanced data privacy and security measures. Mathematics 11, 4189 (2023)","journal-title":"Mathematics"},{"issue":"1","key":"1584_CR6","doi-asserted-by":"crossref","first-page":"12262","DOI":"10.1038\/s41598-022-16388-9","volume":"12","author":"H Annika","year":"2022","unstructured":"Annika, H., Chlebus, G., Meine, H., Thielke, F., Kock, F., Paulus, T., Abolmaali, N., Schenk, A.: Improving automatic liver tumor segmentation in late-phase MRI using multi-model training and 3D convolutional neural networks. Sci. Rep. 12(1), 12262 (2022)","journal-title":"Sci. Rep."},{"key":"1584_CR7","volume":"203","author":"D Shuanhu","year":"2022","unstructured":"Shuanhu, D., Zhao, Y., Liao, M., Yang, Z., Zeng, Y.: Automatic liver tumor segmentation from CT images using hierarchical iterative superpixels and local statistical features. Expert Syst. Appl. 203, 117347 (2022)","journal-title":"Expert Syst. Appl."},{"key":"1584_CR8","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1007\/s42979-024-02991-2","volume":"5","author":"A Biswas","year":"2024","unstructured":"Biswas, A., Maity, S.P., Banik, R., Bhattacharya, P., Debbarma, J.: GAN-driven liver tumor segmentation: enhancing accuracy in biomedical imaging. SN Comput. Sci. 5, 652 (2024)","journal-title":"SN Comput. Sci."},{"key":"1584_CR9","doi-asserted-by":"crossref","first-page":"46201","DOI":"10.1007\/s11042-023-17430-2","volume":"83","author":"U Bhimavarapu","year":"2024","unstructured":"Bhimavarapu, U.: Automatic liver tumor detection and classification using the hyper tangent fuzzy C-means and improved fuzzy SVM. Multimed. Tools Appl. 83, 46201\u201346220 (2024)","journal-title":"Multimed. Tools Appl."},{"issue":"2","key":"1584_CR10","doi-asserted-by":"crossref","first-page":"330","DOI":"10.3390\/cancers15020330","volume":"15","author":"KP Balasubramanian","year":"2023","unstructured":"Balasubramanian, K.P., Lai, W.-C., Seng, G.H., Selvaraj, J.: Apestnet with mask r-cnn for liver tumor segmentation and classification. Cancers 15(2), 330 (2023)","journal-title":"Cancers"},{"key":"1584_CR11","doi-asserted-by":"crossref","first-page":"126182","DOI":"10.1109\/ACCESS.2023.3330919","volume":"11","author":"F Ullah","year":"2023","unstructured":"Ullah, F., Nadeem, M., Abrar, M., Amin, F., Salam, A., Alabrah, A.: Evolutionary model for brain cancer-grading and classification. IEEE Access 11, 126182\u2013126194 (2023)","journal-title":"IEEE Access"},{"issue":"2","key":"1584_CR12","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1002\/ima.22519","volume":"31","author":"M Rela","year":"2020","unstructured":"Rela, M., Rao, S.N., Reddy, P.R.: Optimized segmentation and classification for liver tumor segmentation and classification using opposition-based spotted hyena optimization. Int. J. Imaging Syst. Technol. 31(2), 627\u2013656 (2020)","journal-title":"Int. J. Imaging Syst. Technol."},{"issue":"1","key":"1584_CR13","first-page":"105","volume":"18","author":"F Ullah","year":"2024","unstructured":"Ullah, F., Nadeem, M., Abrar, M.: Revolutionizing brain tumor segmentation in MRI with dynamic fusion of handcrafted features and global pathway-based deep learning. KSII Trans. Internet Inf. Syst. 18(1), 105\u2013125 (2024)","journal-title":"KSII Trans. Internet Inf. Syst."},{"key":"1584_CR14","volume":"83","author":"R Yanhao","year":"2023","unstructured":"Yanhao, R., Zou, D., Xu, W., Zhao, X., Lu, W., He, X.: Bimodal segmentation and classification of endoscopic ultrasonography images for solid pancreatic tumor. Biomed. Signal Process. Control 83, 104591 (2023)","journal-title":"Biomed. Signal Process. Control"},{"key":"1584_CR15","doi-asserted-by":"crossref","unstructured":"Cui, Y., Ren, W., Yang, S., Cao, X., Knoll, A.: IRNeXt: Rethinking Convolutional Network Design for Image Restoration. ICML (2023)","DOI":"10.1109\/ICCV51070.2023.01195"},{"key":"1584_CR16","doi-asserted-by":"crossref","unstructured":"Cui, Y., Ren, W., Cao, X., Knoll, A.: Focal network for image restoration. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp 13001\u201313011 (2023)","DOI":"10.1109\/ICCV51070.2023.01195"},{"key":"1584_CR17","doi-asserted-by":"crossref","unstructured":"Cui, Y., Tao, Y., Bing, Z., Ren, W., Gao, X., Cao, X., Huang, K., Knoll, A.: Selective Frequency Network for Image Restoration. ICLR (2023)","DOI":"10.1109\/ICCV51070.2023.01195"},{"key":"1584_CR18","first-page":"1426","volume":"38","author":"Y Cui","year":"2024","unstructured":"Cui, Y., Ren, W., Knoll, A.: Omni-Kernel network for image restoration. Proc. AAAI Conf. Artif. Intell. 38, 1426\u20131434 (2024)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"issue":"2","key":"1584_CR19","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1109\/TPAMI.2023.3330416","volume":"46","author":"Y Cui","year":"2024","unstructured":"Cui, Y., Ren, W., Cao, X., Knoll, A.: Image restoration via frequency selection. IEEE Trans. Pattern Anal. Mach. Intell. 46(2), 1093\u20131108 (2024)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1584_CR20","doi-asserted-by":"crossref","first-page":"2347","DOI":"10.1007\/s11227-023-05524-5","volume":"80","author":"S Saumiya","year":"2024","unstructured":"Saumiya, S., Franklin, S.W.: Unified automated deep learning framework for segmentation and classification of liver tumors. J. Supercomput. 80, 2347\u20132380 (2024)","journal-title":"J. Supercomput."},{"key":"1584_CR21","doi-asserted-by":"crossref","unstructured":"Anwar, R., Abrar, M., Ullah, F.: Transfer learning in brain tumor classification: challenges, opportunities, and future prospects. In: 2023 14th International Conference on Information and Communication Technology Convergence (ICTC), pp. 24\u201329 (2023)","DOI":"10.1109\/ICTC58733.2023.10392830"},{"key":"1584_CR22","doi-asserted-by":"crossref","first-page":"4576","DOI":"10.1007\/s00330-020-07562-6","volume":"31","author":"M Bing","year":"2021","unstructured":"Bing, M., Ma, J., Duan, S., Xia, Y., Tao, Y., Zhang, L.: Preoperative classification of primary and metastatic liver cancer via machine learning-based ultrasound radiomics. Eur. Radio 31, 4576\u20134586 (2021)","journal-title":"Eur. Radio"},{"issue":"16","key":"1584_CR23","doi-asserted-by":"crossref","first-page":"2650","DOI":"10.3390\/diagnostics13162650","volume":"13","author":"F Ullah","year":"2023","unstructured":"Ullah, F., Nadeem, M., Abrar, M., Al-Razgan, M., Alfakih, T., Amin, F., Salam, A.: Brain tumor segmentation from MRI images using handcrafted convolutional neural network. Diagnostics 13(16), 2650 (2023)","journal-title":"Diagnostics"},{"key":"1584_CR24","doi-asserted-by":"crossref","first-page":"20098","DOI":"10.1038\/s41598-023-46580-4","volume":"13","author":"K Hettihewa","year":"2023","unstructured":"Hettihewa, K., Kobchaisawat, T., Tanpowpong, N., Chalidabhongse, T.H.: MANet: a multi-attention network for automatic liver tumor segmentation in computed tomography (CT) imaging. Sci. Rep. 13, 20098 (2023)","journal-title":"Sci. Rep."},{"key":"1584_CR25","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1007\/s11633-022-1392-6","volume":"20","author":"G Li","year":"2023","unstructured":"Li, G., Hui, X., Li, W., Luo, Y.: Multitask learning with multiscale residual attention for brain tumor segmentation and classification. Mach. Intell. Res. 20, 897\u2013908 (2023)","journal-title":"Mach. Intell. Res."},{"issue":"8","key":"1584_CR26","doi-asserted-by":"crossref","first-page":"368","DOI":"10.3390\/bioengineering9080368","volume":"9","author":"R Hameedur","year":"2022","unstructured":"Hameedur, R., Bukht, T.F.N., Imran, A., Tariq, J., Tu, S., Alzahrani, A.: A deep learning approach for liver and tumor segmentation in CT images using ResUNet. Bioengineering 9(8), 368 (2022)","journal-title":"Bioengineering"},{"key":"1584_CR27","doi-asserted-by":"crossref","first-page":"4729","DOI":"10.1007\/s11042-020-09900-8","volume":"80","author":"R Simranjeet","year":"2021","unstructured":"Simranjeet, R., Alsadoon, A., Prasad, P.W.C., Al-Dala\u2019in, T., Dawoud, A., Alrubaie, A.: Deep learning for liver tumour classification: enhanced loss function. Multimed. Tools Appl. 80, 4729\u20134750 (2021)","journal-title":"Multimed. Tools Appl."},{"key":"1584_CR28","first-page":"81383","volume":"34","author":"DJ Deepak","year":"2024","unstructured":"Deepak, D.J., Kumar, B.S.S.: Liver tumor segmentation using G-Unet and the impact of preprocessing and postprocessing methods. Multimed. Tools Appl. 34, 81383\u201381411 (2024)","journal-title":"Multimed. Tools Appl."},{"issue":"10","key":"1584_CR29","doi-asserted-by":"crossref","first-page":"2965","DOI":"10.1109\/TMI.2022.3175461","volume":"41","author":"Z Rencheng","year":"2022","unstructured":"Rencheng, Z., Wang, Q., Lv, S., Li, C., Wang, C., Chen, W., Wang, H.: Automatic liver tumor segmentation on dynamic contrast enhanced MRI using 4D information: deep learning model based on 3D convolution and convolutional LSTM. IEEE Trans. Med. Imaging 41(10), 2965\u20132976 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1584_CR30","volume":"134","author":"U Budak","year":"2020","unstructured":"Budak, U., Guo, Y., Tanyildizi, E., Sengur, A.: Cascaded deep convolutional encoder\u2013decoder neural networks for EfficientLiver tumor segmentation. Med. Hypotheses 134, 109431 (2020)","journal-title":"Med. Hypotheses"},{"key":"1584_CR31","doi-asserted-by":"crossref","first-page":"76056","DOI":"10.1109\/ACCESS.2020.2988647","volume":"8","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y., Jiang, B., Wu, J., Ji, D.: Deep learning initialized and gradient enhanced level-set based segmentation for liver tumor from CT images. IEEE Access 8, 76056\u201376068 (2020)","journal-title":"IEEE Access"},{"key":"1584_CR32","volume":"150","author":"Y Chen","year":"2022","unstructured":"Chen, Y., Zheng, C., Hu, F., Zhou, T., Feng, L., Xu, G., Yi, Z., Zhang, X.: Efficient two-step liver and tumour segmentation on abdominal CT via deep learning and a conditional random field. Comput. Biol. Med. 150, 106076 (2022)","journal-title":"Comput. Biol. Med."},{"key":"1584_CR33","volume":"6","author":"RV Manjunath","year":"2022","unstructured":"Manjunath, R.V., Kwadiki, K.: Automatic liver and tumour segmentation from CT images using Deep learning algorithm. Results Control Optim. 6, 100087 (2022)","journal-title":"Results Control Optim."},{"issue":"3","key":"1584_CR34","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.jvir.2021.12.017","volume":"33","author":"R Malpani","year":"2022","unstructured":"Malpani, R., Petty, C.W., Yang, J., Bhatt, N., Zeevi, T., Chockalingam, V., Raju, R., Petukhova-Greenstein, A., Santana, J.G., Schlachter, T.R., Madoff, D.C., Chapiro, J., Duncan, J., Lin, M.: Quantitative automated segmentation of lipiodol deposits on cone-beam CT imaging acquired during transarterial chemoembolization for liver tumors: a deep learning approach. J. Vasc. Interv. Radiol. 33(3), 324\u2013332 (2022)","journal-title":"J. Vasc. Interv. Radiol."},{"key":"1584_CR35","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104305","volume":"80","author":"DT Kushnure","year":"2023","unstructured":"Kushnure, D.T., Tyagi, S., Talbar, S.N.: LiM-Net: lightweight multi-level multiscale network with deep residual learning for automatic liver segmentation in CT images. Biomed. Signal Process. Control 80, 104305 (2023)","journal-title":"Biomed. Signal Process. Control"},{"issue":"3","key":"1584_CR36","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1109\/JBHI.2018.2886276","volume":"23","author":"E Trivizakis","year":"2019","unstructured":"Trivizakis, E., Manikis, G.C., Nikifor, K.: Extending 2-D convolutional neural networks to 3-D for advancing deep learning cancer classification with application to MRI liver tumor differentiation. IEEE J. Biomed. Health. Inf. 23(3), 923\u2013930 (2019)","journal-title":"IEEE J. Biomed. Health. Inf."},{"issue":"3","key":"1584_CR37","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1080\/21681163.2022.2099300","volume":"11","author":"S Roy","year":"2022","unstructured":"Roy, S., Sayan, S., Mukherjee, P., Roy, A.H.: An automated liver tumour segmentation and classification model by deep learning based approaches. Comput. Methods Biomech. Biomed. Eng. Imaging Visual. 11(3), 638\u2013650 (2022)","journal-title":"Comput. Methods Biomech. Biomed. Eng. Imaging Visual."},{"key":"1584_CR38","volume":"8","author":"S Kolli","year":"2024","unstructured":"Kolli, S., Parvathala, B.R., Krishna, A.V.P.: A novel liver tumor classification using improved probabilistic neural networks with Bayesian optimization. e-Prime Adv. Electr. Eng. Electron. 8, 100514 (2024)","journal-title":"e-Prime Adv. Electr. Eng. Electron."},{"key":"1584_CR39","doi-asserted-by":"crossref","first-page":"100632","DOI":"10.1016\/j.prime.2024.100632","volume":"9","author":"M Kasipandi","year":"2024","unstructured":"Kasipandi, M., Chandran, C.P., Rajathi, S.: A novel liver tumor segmentation of adverse propagation advanced Swin transformer network with mask region-based convolutional neural networks. e-Prime Adv. Electr. Eng. Electron. 9, 100632 (2024)","journal-title":"e-Prime Adv. Electr. Eng. Electron."},{"key":"1584_CR40","doi-asserted-by":"crossref","first-page":"2515","DOI":"10.1007\/s42235-024-00562-y","volume":"21","author":"J Sun","year":"2024","unstructured":"Sun, J., Wang, B., Wu, X., Tang, C., Wang, S., Zhang, Y.: MAPFUNet: multi-attention perception-fusion U-Net for liver tumor segmentation. J. Bionic Eng. 21, 2515\u20132539 (2024)","journal-title":"J. Bionic Eng."},{"key":"1584_CR41","first-page":"5351","volume":"8","author":"SC Bandaru","year":"2024","unstructured":"Bandaru, S.C., Mohan, G.B., Kumar, R.P., Altalbe, A.: SwinGALE: fusion of swin transformer and attention mechanism for GAN-augmented liver tumor classification with enhanced deep learning. Int. J. Inf. Technol. 8, 5351\u20135369 (2024)","journal-title":"Int. J. Inf. Technol."},{"issue":"19","key":"1584_CR42","doi-asserted-by":"crossref","first-page":"10057","DOI":"10.3390\/app121910057","volume":"12","author":"AK Abasi","year":"2022","unstructured":"Abasi, A.K., Makhadmeh, S.N., Al-Betar, M.A., Alomari, O.A., Awadallah, M.A., Alyasseri, Z.A.A., Doush, I.A., Elnagar, A., Alkhammash, E.H., Hadjouni, M.: Lemurs optimizer: a new metaheuristic algorithm for global optimization. Appl. Sci. 12(19), 10057 (2022)","journal-title":"Appl. Sci."},{"issue":"4","key":"1584_CR43","doi-asserted-by":"crossref","first-page":"1467","DOI":"10.1109\/TITS.2019.2911727","volume":"21","author":"U Kamal","year":"2019","unstructured":"Kamal, U., Tonmoy, T.I., Das, S., Hasan, M.K.: Automatic traffic sign detection and recognition using SegU-Net and a modified Tversky loss function with L1-constraint. IEEE Trans. Intell. Transp. Syst. 21(4), 1467\u20131479 (2019)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"1584_CR44","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/9409508","author":"CH Yang","year":"2021","unstructured":"Yang, C.H., Ren, J.H., Huang, H.C., Chuang, L.Y., Chang, P.Y.: Deep hybrid convolutional neural network for segmentation of melanoma skin lesion. Comput. Intell. Neurosci. (2021). https:\/\/doi.org\/10.1155\/2021\/9409508","journal-title":"Comput. Intell. Neurosci."},{"key":"1584_CR45","doi-asserted-by":"crossref","unstructured":"Kumar, R.S., Dhivyasri, R., Jothika, P., Surya, G.: Detection and Classification of Neuro-degenerative Disease via EfficientNetB7, pp. 223-234 (2023)","DOI":"10.1007\/978-981-97-0700-3_17"},{"issue":"7","key":"1584_CR46","doi-asserted-by":"crossref","first-page":"4949","DOI":"10.1109\/TII.2020.2967557","volume":"16","author":"D Peng","year":"2020","unstructured":"Peng, D., Wang, H., Liu, Z., Zhang, W., Zuo, M.J., Chen, J.: Multibranch and multiscale CNN for fault diagnosis of wheelset bearings under strong noise and variable load condition. IEEE Trans. Ind. Inf. 16(7), 4949\u20134960 (2020)","journal-title":"IEEE Trans. Ind. Inf."},{"key":"1584_CR47","doi-asserted-by":"crossref","unstructured":"Kim, J., El-Khamy, M., Lee, J.: Residual LSTM: design of a deep recurrent architecture for distant speech recognition. arXiv preprint (2017)","DOI":"10.21437\/Interspeech.2017-477"},{"issue":"10","key":"1584_CR48","doi-asserted-by":"crossref","first-page":"5887","DOI":"10.1002\/int.22535","volume":"36","author":"B Abdollahzadeh","year":"2021","unstructured":"Abdollahzadeh, B., Gharehchopogh, F.S., Mirjalili, S.: Artificial gorilla troops optimizer: a new nature-inspired metaheuristic algorithm for global optimization problems. Int. J. Intell. Syst. 36(10), 5887\u20135958 (2021)","journal-title":"Int. J. Intell. Syst."},{"key":"1584_CR49","doi-asserted-by":"crossref","first-page":"1531","DOI":"10.1007\/s10489-020-01893-z","volume":"51","author":"FA Hashim","year":"2021","unstructured":"Hashim, F.A., Hussain, K., Houssein, E.H., Mabrouk, M.S., Al-Atabany, W.: Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Appl. Intell. 51, 1531\u20131551 (2021)","journal-title":"Appl. Intell."},{"issue":"17","key":"1584_CR50","doi-asserted-by":"crossref","first-page":"8650","DOI":"10.3390\/app12178650","volume":"12","author":"MW Sabir","year":"2022","unstructured":"Sabir, M.W., Khan, Z., Saad, N.M., Khan, D.M., Al-Khasawneh, M.A., Perveen, K., Qayyum, A., Ali, S.S.A.: Segmentation of liver tumor in CT scan using ResU-Net. Appl. Sci. 12(17), 8650 (2022)","journal-title":"Appl. Sci."},{"key":"1584_CR51","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint (2021)"},{"key":"1584_CR52","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.procs.2021.01.025","volume":"179","author":"D Sarwinda","year":"2021","unstructured":"Sarwinda, D., Paradisa, R.H., Bustamam, A., Anggia, P.: Deep learning in image classification using residual network (ResNet) variants for detection of colorectal cancer. Proc. Comput. Sci. 179, 423\u2013431 (2021)","journal-title":"Proc. Comput. Sci."},{"issue":"6","key":"1584_CR53","first-page":"463","volume":"88","author":"B Alotaibi","year":"2020","unstructured":"Alotaibi, B., Alotaibi, M.: A hybrid deep ResNet and inception model for hyperspectral image classification. PFG-J. Photogram. Remote Sens. Geoinf. Sci. 88(6), 463\u2013476 (2020)","journal-title":"PFG-J. Photogram. Remote Sens. Geoinf. Sci."},{"key":"1584_CR54","doi-asserted-by":"crossref","first-page":"012143","DOI":"10.1088\/1742-6596\/1651\/1\/012143","volume":"1651","author":"Z Zhong","year":"2020","unstructured":"Zhong, Z., Zheng, M., Mai, H., Zhao, J., Liu, X.: Cancer image classification based on DenseNet model. J. Phys. Conf. Ser. 1651, 012143 (2020)","journal-title":"J. Phys. Conf. Ser."},{"issue":"8","key":"1584_CR55","first-page":"1","volume":"6","author":"M Liu","year":"2023","unstructured":"Liu, M.: Method of rectal tumor segmentation based on ResUnet++. Acad. J. Comput. Inf. Sci. 6(8), 1\u20137 (2023)","journal-title":"Acad. J. Comput. Inf. Sci."},{"issue":"12","key":"1584_CR56","doi-asserted-by":"crossref","first-page":"2663","DOI":"10.1109\/TMI.2018.2845918","volume":"37","author":"X Li","year":"2018","unstructured":"Li, X., Chen, H., Qi, X., Dou, Q., Fu, C.W., Heng, P.A.: H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans. Med. Imaging 37(12), 2663\u20132674 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1584_CR57","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/7602384","author":"W Wang","year":"2020","unstructured":"Wang, W., Li, Y., Zou, T., Wang, X., You, J., Luo, Y.: A novel image classification approach via dense-MobileNet models. Mobile Inf. Syst. (2020). https:\/\/doi.org\/10.1155\/2020\/7602384","journal-title":"Mobile Inf. Syst."},{"key":"1584_CR58","doi-asserted-by":"publisher","DOI":"10.1142\/S0218001422400055","author":"R Jose","year":"2022","unstructured":"Jose, R., Chacko, S., Jayakumar, J., Jarin, T.: Liver tumor classification using optimal opposition-based grey wolf optimization. Int. J. Pattern Recogn. Artif. Intell. (2022). https:\/\/doi.org\/10.1142\/S0218001422400055","journal-title":"Int. J. Pattern Recogn. Artif. Intell."},{"issue":"2","key":"1584_CR59","doi-asserted-by":"crossref","first-page":"143","DOI":"10.52866\/ijcsm.2023.02.02.012","volume":"4","author":"R Pandian","year":"2023","unstructured":"Pandian, R., Shanthi, D., Selvaganesh, N.: An articulate heart attack detection system using mine blast optimization (MBO) based multilayer perceptron neural network (MLPNN) model. Iraqi J. Comput. Sci. Math. 4(2), 143\u2013155 (2023)","journal-title":"Iraqi J. Comput. Sci. Math."}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01584-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-024-01584-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01584-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T17:16:56Z","timestamp":1738603016000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-024-01584-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,27]]},"references-count":59,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["1584"],"URL":"https:\/\/doi.org\/10.1007\/s11554-024-01584-9","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,27]]},"assertion":[{"value":"22 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 November 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 competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"8"}}