{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T11:18:25Z","timestamp":1784632705821,"version":"3.55.0"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"26","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"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\/s11042-024-18974-7","type":"journal-article","created":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T09:12:10Z","timestamp":1711962730000},"page":"68181-68208","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["An efficient Dense-Resnet for multimodal image fusion using medical image"],"prefix":"10.1007","volume":"83","author":[{"given":"Tanima","family":"Ghosh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"N.","family":"Jayanthi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,1]]},"reference":[{"key":"18974_CR1","doi-asserted-by":"publisher","first-page":"2483","DOI":"10.1007\/s12652-020-02386-0","volume":"12","author":"M Kaur","year":"2021","unstructured":"Kaur M, Singh D (2021) Multi-modality medical image fusion technique using multi-objective differential evolution based deep neural networks. J Ambient Intell Humaniz Comput 12:2483\u20132493","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"18974_CR2","doi-asserted-by":"publisher","first-page":"67634","DOI":"10.1109\/ACCESS.2021.3075953","volume":"9","author":"L Wang","year":"2021","unstructured":"Wang L, Zhang J, Liu Y, Mi J, Zhang J (2021) Multimodal medical image fusion based on Gabor representation combination of multi-CNN and fuzzy neural network. IEEE Access 9:67634\u201367647","journal-title":"IEEE Access"},{"issue":"1","key":"18974_CR3","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1049\/iet-ipr.2018.5720","volume":"13","author":"L Chang","year":"2019","unstructured":"Chang L, Feng X, Zhu X, Zhang R, He R, Xu C (2019) CT and MRI image fusion based on multiscale decomposition method and hybrid approach. IET Image Proc 13(1):83\u201388","journal-title":"IET Image Proc"},{"key":"18974_CR4","doi-asserted-by":"crossref","unstructured":"Lou XC, Feng X (2021) Multimodal medical image fusion based on multiple latent low-rank representation, Comput Math Meth Med, 1\u201316","DOI":"10.1155\/2021\/1544955"},{"key":"18974_CR5","doi-asserted-by":"crossref","unstructured":"Nandhini Abirami R, Durai Raj Vincent PM, Srinivasan K, Manic KS, Chang CY (2022) Multimodal medical image fusion of positron emission tomography and magnetic resonance imaging using generative adversarial networks. Behav Neurol","DOI":"10.1155\/2022\/6878783"},{"key":"18974_CR6","doi-asserted-by":"publisher","first-page":"638976","DOI":"10.3389\/fnins.2021.638976","volume":"15","author":"Y Li","year":"2021","unstructured":"Li Y, Zhao J, Lv Z, Pan Z (2021) Multimodal medical supervised image fusion method by CNN. Front Neurosci 15:638976","journal-title":"Front Neurosci"},{"key":"18974_CR7","doi-asserted-by":"crossref","unstructured":"Huang B, Yang F, Yin M, Mo X, Zhong C (2020) A review of multimodal medical image fusion techniques. Comput Math Meth Med","DOI":"10.1155\/2020\/8279342"},{"key":"18974_CR8","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.inffus.2018.07.010","volume":"48","author":"B Meher","year":"2019","unstructured":"Meher B, Agrawal S, Panda R, Abraham A (2019) A survey on region based image fusion methods. Information Fusion 48:119\u2013132","journal-title":"Information Fusion"},{"issue":"14","key":"18974_CR9","doi-asserted-by":"publisher","first-page":"2124","DOI":"10.3390\/electronics11142124","volume":"11","author":"MM Almasri","year":"2022","unstructured":"Almasri MM, Alajlan AM (2022) Artificial Intelligence-Based Multimodal Medical Image Fusion Using Hybrid S2 Optimal CNN. Electronics 11(14):2124","journal-title":"Electronics"},{"key":"18974_CR10","doi-asserted-by":"publisher","first-page":"104048","DOI":"10.1016\/j.compbiomed.2020.104048","volume":"126","author":"J Fu","year":"2020","unstructured":"Fu J, Li W, Du J, Xiao B (2020) Multimodal medical image fusion via laplacian pyramid and convolutional neural network reconstruction with local gradient energy strategy. Comput Biol Med 126:104048","journal-title":"Comput Biol Med"},{"issue":"3","key":"18974_CR11","doi-asserted-by":"publisher","first-page":"1076","DOI":"10.1109\/TIP.2016.2633863","volume":"26","author":"P Hill","year":"2016","unstructured":"Hill P, Al-Mualla ME, Bull D (2016) Perceptual image fusion using wavelets. IEEE Trans Image Process 26(3):1076\u20131088","journal-title":"IEEE Trans Image Process"},{"issue":"12","key":"18974_CR12","doi-asserted-by":"publisher","first-page":"3450","DOI":"10.1109\/TBME.2012.2217493","volume":"59","author":"S Li","year":"2012","unstructured":"Li S, Yin H, Fang L (2012) Group-sparse representation with dictionary learning for medical image denoising and fusion. IEEE Trans Biomed Eng 59(12):3450\u20133459","journal-title":"IEEE Trans Biomed Eng"},{"key":"18974_CR13","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.inffus.2014.09.004","volume":"24","author":"Y Liu","year":"2015","unstructured":"Liu Y, Liu S, Wang Z (2015) A general framework for image fusion based on multi-scale transform and sparse representation. Information fusion 24:147\u2013164","journal-title":"Information fusion"},{"key":"18974_CR14","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.ijcce.2020.12.004","volume":"2","author":"Y Li","year":"2021","unstructured":"Li Y, Zhao J, Lv Z, Li J (2021) Medical image fusion method by deep learning. Intl J Cognit Comput Eng 2:21\u201329","journal-title":"Intl J Cognit Comput Eng"},{"key":"18974_CR15","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.inffus.2016.12.001","volume":"36","author":"Y Liu","year":"2017","unstructured":"Liu Y, Chen X, Peng H, Wang Z (2017) Multi-focus image fusion with a deep convolutional neural network. Information Fusion 36:191\u2013207","journal-title":"Information Fusion"},{"issue":"6","key":"18974_CR16","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1038\/nn1255","volume":"7","author":"W Li","year":"2004","unstructured":"Li W, Pi\u00ebch V, Gilbert CD (2004) Perceptual learning and top-down influences in primary visual cortex. Nat Neurosci 7(6):651\u2013657","journal-title":"Nat Neurosci"},{"issue":"5","key":"18974_CR17","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1038\/nrn3476","volume":"14","author":"CD Gilbert","year":"2013","unstructured":"Gilbert CD, Li W (2013) Top-down influences on visual processing. Nat Rev Neurosci 14(5):350\u2013363","journal-title":"Nat Rev Neurosci"},{"key":"18974_CR18","doi-asserted-by":"publisher","first-page":"5134","DOI":"10.1109\/TIP.2022.3193288","volume":"31","author":"W Tang","year":"2022","unstructured":"Tang W, He F, Liu Y, Duan Y (2022) MATR: multimodal medical image fusion via multiscale adaptive transformer. IEEE Trans Image Process 31:5134\u20135149","journal-title":"IEEE Trans Image Process"},{"key":"18974_CR19","doi-asserted-by":"crossref","unstructured":"Maheshan CM, Prasanna Kumar H (2020) Performance of image pre-processing filters for noise removal in transformer oil images at different temperatures. SN Appl Sci, 2: 1\u20137","DOI":"10.1007\/s42452-019-1800-x"},{"issue":"3","key":"18974_CR20","doi-asserted-by":"publisher","first-page":"151","DOI":"10.7555\/JBR.34.20190026","volume":"34","author":"IB Slimen","year":"2020","unstructured":"Slimen IB, Boubchir L, Mbarki Z, Seddik H (2020) EEG epileptic seizure detection and classification based on dual-tree complex wavelet transform and machine learning algorithms. J Biomed Res 34(3):151","journal-title":"J Biomed Res"},{"key":"18974_CR21","doi-asserted-by":"crossref","unstructured":"Zhang Z, Fu H, Dai H, Shen J, Pang Y, Shao L (2019) Et-net: A generic edge-attention guidance network for medical image segmentation. In Proceedings of 22nd International Conference, In Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019, Shenzhen, China, Part I vol.22, Springer International Publishing, pp. 442\u2013450, October 13\u201317","DOI":"10.1007\/978-3-030-32239-7_49"},{"issue":"8","key":"18974_CR22","doi-asserted-by":"publisher","first-page":"2988","DOI":"10.3390\/s22082988","volume":"22","author":"A Vulli","year":"2022","unstructured":"Vulli A, Srinivasu PN, Sashank MSK, Shafi J, Choi J, Ijaz MF (2022) Fine-tuned DenseNet-169 for breast cancer metastasis prediction using FastAI and 1-cycle policy. Sensors 22(8):2988","journal-title":"Sensors"},{"key":"18974_CR23","doi-asserted-by":"publisher","first-page":"111793","DOI":"10.1016\/j.enconman.2019.111793","volume":"198","author":"Z Chen","year":"2019","unstructured":"Chen Z, Chen Y, Wu L, Cheng S, Lin P (2019) Deep residual network based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions. Energy Convers Manage 198:111793","journal-title":"Energy Convers Manage"},{"key":"18974_CR24","doi-asserted-by":"crossref","unstructured":"Bhaladhare PR, Jinwala DC (2014) A clustering approach for the-diversity model in privacy preserving data mining using fractional calculus-bacterial foraging optimization algorithm. Adv Comput Eng","DOI":"10.1155\/2014\/396529"},{"key":"18974_CR25","unstructured":"BRATS 2020 dataset is taken from \u201chttps:\/\/www.kaggle.com\/datasets\/awsaf49\/brats2020-training-data?select=BraTS20+Training+Metadata.csv\u201d, accessed on July 2023"},{"key":"18974_CR26","doi-asserted-by":"publisher","first-page":"5134","DOI":"10.1109\/TIP.2022.3193288","volume":"31","author":"W Tang","year":"2022","unstructured":"Tang W, Fazhi F, Liu Y, Duan Y (2022) MATR: Multimodal Medical Image Fusion via Multiscale Adaptive Transformer. IEEE Trans Image Process 31:5134\u20135149","journal-title":"IEEE Trans Image Process"},{"key":"18974_CR27","doi-asserted-by":"crossref","unstructured":"Weiwei Kong, Chi Li, Yang Lei (2022) Multimodal medical image fusion using convolutional neural network and extreme learning machine. Front Neurorob, Vol. 16, November","DOI":"10.3389\/fnbot.2022.1050981"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18974-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-18974-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18974-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,22]],"date-time":"2024-07-22T01:20:26Z","timestamp":1721611226000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-18974-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,1]]},"references-count":27,"journal-issue":{"issue":"26","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["18974"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-18974-7","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,1]]},"assertion":[{"value":"22 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 September 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 March 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 April 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":"The authors declare no conflict of interest.","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"}}]}}