{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T20:17:13Z","timestamp":1783109833441,"version":"3.54.6"},"reference-count":62,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100017700","name":"Henan Provincial Science and Technology Research Project","doi-asserted-by":"publisher","award":["252102211015"],"award-info":[{"award-number":["252102211015"]}],"id":[{"id":"10.13039\/501100017700","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62472145"],"award-info":[{"award-number":["62472145"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003488","name":"Henan Polytechnic University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003488","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.eswa.2026.132910","type":"journal-article","created":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T15:39:07Z","timestamp":1779464347000},"page":"132910","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["IMMNet: A real-time semantic segmentation network integrating multi-path Mamba and multi-level local features"],"prefix":"10.1016","volume":"329","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3376-6649","authenticated-orcid":false,"given":"Shan","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7090-9127","authenticated-orcid":false,"given":"Kaiyu","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4641-6685","authenticated-orcid":false,"given":"Jiajia","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7378-3478","authenticated-orcid":false,"given":"Fukai","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9243-5009","authenticated-orcid":false,"given":"Zhanqiang","family":"Huo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.eswa.2026.132910_bib0001","first-page":"968","article-title":"Rare-event prediction in imbalanced data: A unified evaluation and optimization framework for high-risk systems","volume":"9","author":"Abdulrazaq","year":"2023","journal-title":"Communication In Physical Sciences"},{"issue":"12","key":"10.1016\/j.eswa.2026.132910_bib0002","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"2","key":"10.1016\/j.eswa.2026.132910_bib0003","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.patrec.2008.04.005","article-title":"Semantic object classes in video: A high-definition ground truth database","volume":"30","author":"Brostow","year":"2009","journal-title":"Pattern Recognition Letters"},{"key":"10.1016\/j.eswa.2026.132910_bib0004","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2014). Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv preprint arXiv: 1412.7062,."},{"issue":"4","key":"10.1016\/j.eswa.2026.132910_bib0005","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.eswa.2026.132910_bib0006","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., & Adam, H. (2019). Rethinking atrous convolution for semantic image segmentation. arxiv 2017. arXiv preprint arXiv: 1706.05587, 2, 1."},{"key":"10.1016\/j.eswa.2026.132910_bib0007","series-title":"Proceedings of the european conference on computer vision (ECCV)","first-page":"801","article-title":"Encoder-decoder with atrous separable convolution for semantic image segmentation","author":"Chen","year":"2018"},{"key":"10.1016\/j.eswa.2026.132910_bib0008","unstructured":"Contributors, M. (2020). MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark."},{"key":"10.1016\/j.eswa.2026.132910_bib0009","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"3213","article-title":"The cityscapes dataset for semantic urban scene understanding","author":"Cordts","year":"2016"},{"key":"10.1016\/j.eswa.2026.132910_bib0010","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S. et al. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv: 2010.11929,."},{"issue":"2","key":"10.1016\/j.eswa.2026.132910_bib0011","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The pascal visual object classes (voc) challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"International Journal of Computer Vision"},{"key":"10.1016\/j.eswa.2026.132910_bib0012","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"9716","article-title":"Rethinking bisenet for real-time semantic segmentation","author":"Fan","year":"2021"},{"key":"10.1016\/j.eswa.2026.132910_bib0013","series-title":"Proceedings of the computer vision and pattern recognition conference","first-page":"19077","article-title":"SegMAN: Omni-scale context modeling with state space models and local attention for semantic segmentation","author":"Fu","year":"2025"},{"key":"10.1016\/j.eswa.2026.132910_bib0014","doi-asserted-by":"crossref","first-page":"1978","DOI":"10.1109\/TIP.2023.3261747","article-title":"CtcNet: A cnn-transformer cooperation network for face image super-resolution","volume":"32","author":"Gao","year":"2023","journal-title":"IEEE Transactions on Image Processing"},{"key":"10.1016\/j.eswa.2026.132910_bib0015","unstructured":"Ge, C., Ding, X., Tong, Z., Yuan, L., Wang, J., Song, Y., & Luo, P. (2023). Advancing vision transformers with group-mix attention. arXiv preprint arXiv: 2311.15157,."},{"key":"10.1016\/j.eswa.2026.132910_bib0016","series-title":"Proceedings of the IEEE\/CVF international conference on computer vision","first-page":"5961","article-title":"Flatten transformer: Vision transformer using focused linear attention","author":"Han","year":"2023"},{"key":"10.1016\/j.eswa.2026.132910_bib0017","series-title":"European conference on computer vision","first-page":"124","article-title":"Agent attention: On the integration of softmax and linear attention","author":"Han","year":"2024"},{"key":"10.1016\/j.eswa.2026.132910_bib0018","unstructured":"Hatamizadeh, A., Heinrich, G., Yin, H., Tao, A., Alvarez, J. M., Kautz, J., & Molchanov, P. (2023). FasterViT: Fast vision transformers with hierarchical attention. arXiv preprint arXiv: 2306.06189,."},{"key":"10.1016\/j.eswa.2026.132910_bib0019","series-title":"Proceedings of the computer vision and pattern recognition conference","first-page":"4497","article-title":"MobileMamba: Lightweight multi-receptive visual mamba network","author":"He","year":"2025"},{"key":"10.1016\/j.eswa.2026.132910_bib0020","unstructured":"Hong, Y., Pan, H., Sun, W., & Jia, Y. (2021). Deep dual-resolution networks for real-time and accurate semantic segmentation of road scenes. arXiv preprint arXiv: 2101.06085,."},{"issue":"12","key":"10.1016\/j.eswa.2026.132910_bib0021","doi-asserted-by":"crossref","first-page":"8274","DOI":"10.1109\/TPAMI.2024.3401450","article-title":"Conv2Former: A simple transformer-style convnet for visual recognition","volume":"46","author":"Hou","year":"2024","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.eswa.2026.132910_bib0022","series-title":"European conference on computer vision","first-page":"12","article-title":"LocalMamba: Visual state space model with windowed selective scan","author":"Huang","year":"2024"},{"key":"10.1016\/j.eswa.2026.132910_bib0023","series-title":"2024\u202fIEEE 7th international conference on multimedia information processing and retrieval (MIPR)","first-page":"75","article-title":"Segformer++: Efficient token-merging strategies for high-resolution semantic segmentation","author":"Kienzle","year":"2024"},{"key":"10.1016\/j.eswa.2026.132910_bib0024","series-title":"Proceedings of the computer vision and pattern recognition conference","first-page":"14923","article-title":"Efficientvim: Efficient vision mamba with hidden state mixer based state space duality","author":"Lee","year":"2025"},{"key":"10.1016\/j.eswa.2026.132910_bib0025","series-title":"European conference on computer vision","first-page":"314","article-title":"MTMamba: Enhancing multi-task dense scene understanding by mamba-based decoders","author":"Lin","year":"2024"},{"key":"10.1016\/j.eswa.2026.132910_bib0026","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"1925","article-title":"RefineNet: Multi-path refinement networks for high-resolution semantic segmentation","author":"Lin","year":"2017"},{"key":"10.1016\/j.eswa.2026.132910_bib0027","series-title":"Proceedings of the computer vision and pattern recognition conference","first-page":"29406","article-title":"SCSegamba: Lightweight structure-aware vision mamba for crack segmentation in structures","author":"Liu","year":"2025"},{"key":"10.1016\/j.eswa.2026.132910_bib0028","unstructured":"Liu, M., Dan, J., Lu, Z., Yu, Y., Li, Y., & Li, X. (2024a). Cm-uNet: Hybrid cnn-mamba unet for remote sensing image semantic segmentation. arXiv preprint arXiv: 2405.10530,."},{"key":"10.1016\/j.eswa.2026.132910_bib0029","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"14420","article-title":"EfficientViT: Memory efficient vision transformer with cascaded group attention","author":"Liu","year":"2023"},{"key":"10.1016\/j.eswa.2026.132910_bib0030","doi-asserted-by":"crossref","first-page":"103031","DOI":"10.52202\/079017-3273","article-title":"VMamba: Visual state space model","volume":"37","author":"Liu","year":"2024","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.132910_bib0031","first-page":"1","article-title":"Rs 3 mamba: Visual state space model for remote sensing image semantic segmentation","volume":"21","author":"Ma","year":"2024","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"key":"10.1016\/j.eswa.2026.132910_bib0032","doi-asserted-by":"crossref","unstructured":"Nguyen, T.-N.-Q., Ho, Q.-H., Nguyen, D.-T., Le, H.-M.-Q., Pham, V.-T., & Tran, T.-T. (2024). MambaU-Lite: A lightweight model based on mamba and integrated channel-spatial attention for skin lesion segmentation. arXiv preprint arXiv: 2412.01405,.","DOI":"10.1007\/978-981-95-1746-6_6"},{"key":"10.1016\/j.eswa.2026.132910_bib0033","series-title":"European conference on computer vision","first-page":"239","article-title":"Context-guided spatial feature reconstruction for efficient semantic segmentation","author":"Ni","year":"2024"},{"key":"10.1016\/j.eswa.2026.132910_bib0034","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"4061","article-title":"HyperSeg: Patch-wise hypernetwork for real-time semantic segmentation","author":"Nirkin","year":"2021"},{"key":"10.1016\/j.eswa.2026.132910_bib0035","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"12607","article-title":"In defense of pre-trained imagenet architectures for real-time semantic segmentation of road-driving images","author":"Orsic","year":"2019"},{"key":"10.1016\/j.eswa.2026.132910_bib0036","unstructured":"Paszke, A., Chaurasia, A., Kim, S., & Culurciello, E. (2016). ENet: A deep neural network architecture for real-time semantic segmentation. arXiv preprint arXiv: 1606.02147,."},{"key":"10.1016\/j.eswa.2026.132910_bib0037","first-page":"8024","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume":"32","author":"Paszke","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.132910_bib0038","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"6443","article-title":"EfficientVMamba: Atrous selective scan for light weight visual mamba","volume":"vol. 39","author":"Pei","year":"2025"},{"key":"10.1016\/j.eswa.2026.132910_bib0039","unstructured":"Peng, J., Liu, Y., Tang, S., Hao, Y., Chu, L., Chen, G., Wu, Z., Chen, Z., Yu, Z., Du, Y. et al. (2022). Pp-LiteSeg: A superior real-time semantic segmentation model. arXiv preprint arXiv: 2204.02681,."},{"key":"10.1016\/j.eswa.2026.132910_bib0040","unstructured":"Poudel, R. P. K., Liwicki, S., & Cipolla, R. (2019). Fast-SCNN: Fast semantic segmentation network. arXiv preprint arXiv: 1902.04502,."},{"issue":"1","key":"10.1016\/j.eswa.2026.132910_bib0041","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1109\/TITS.2017.2750080","article-title":"ErfNet: Efficient residual factorized convnet for real-time semantic segmentation","volume":"19","author":"Romera","year":"2017","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"3","key":"10.1016\/j.eswa.2026.132910_bib0042","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"International Journal of Computer Vision"},{"key":"10.1016\/j.eswa.2026.132910_bib0043","doi-asserted-by":"crossref","first-page":"25687","DOI":"10.52202\/079017-0808","article-title":"Multi-scale VMamba: Hierarchy in hierarchy visual state space model","volume":"37","author":"Shi","year":"2024","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.132910_bib0044","doi-asserted-by":"crossref","unstructured":"Si, H., Zhang, Z., Lv, F., Yu, G., & Lu, F. (2019). Real-time semantic segmentation via multiply spatial fusion network. arXiv preprint arXiv: 1911.07217,.","DOI":"10.5244\/C.34.153"},{"key":"10.1016\/j.eswa.2026.132910_bib0045","doi-asserted-by":"crossref","unstructured":"Tan, W., Geng, Y., & Xie, X. (2023). FMViT: A multiple-frequency mixing vision transformer. arXiv preprint arXiv: 2311.05707,.","DOI":"10.3233\/FAIA240476"},{"key":"10.1016\/j.eswa.2026.132910_bib0046","series-title":"The eleventh international conference on learning representations","article-title":"SeaFormer: Squeeze-enhanced axial transformer for mobile semantic segmentation","author":"Wan","year":"2023"},{"key":"10.1016\/j.eswa.2026.132910_bib0047","doi-asserted-by":"crossref","first-page":"7423","DOI":"10.52202\/068431-0539","article-title":"RtFormer: Efficient design for real-time semantic segmentation with transformer","volume":"35","author":"Wang","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.132910_bib0048","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1109\/TIP.2020.3042065","article-title":"CgNet: A light-weight context guided network for semantic segmentation","volume":"30","author":"Wu","year":"2020","journal-title":"IEEE Transactions on Image Processing"},{"key":"10.1016\/j.eswa.2026.132910_bib0049","unstructured":"Xiao, C., Li, M., Zhang, Z., Meng, D., & Zhang, L. (2024). Spatial-Mamba: Effective visual state space models via structure-aware state fusion. arXiv preprint arXiv: 2410.15091,."},{"key":"10.1016\/j.eswa.2026.132910_bib0050","first-page":"12077","article-title":"SegFormer: Simple and efficient design for semantic segmentation with transformers","volume":"34","author":"Xie","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.132910_bib0051","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"19529","article-title":"PidNet: A real-time semantic segmentation network inspired by pid controllers","author":"Xu","year":"2023"},{"key":"10.1016\/j.eswa.2026.132910_bib0052","unstructured":"Yan, H., Wu, M., & Zhang, C. (2024). Multi-scale representations by varying window attention for semantic segmentation. arXiv preprint arXiv: 2404.16573,."},{"key":"10.1016\/j.eswa.2026.132910_bib0053","unstructured":"Yang, C., Chen, Z., Espinosa, M., Ericsson, L., Wang, Z., Liu, J., & Crowley, E. J. (2024a). PlainMamba: Improving non-hierarchical mamba in visual recognition. arXiv preprint arXiv: 2403.17695,."},{"key":"10.1016\/j.eswa.2026.132910_bib0054","doi-asserted-by":"crossref","unstructured":"Yang, G., Wang, Y., & Shi, D. (2024b). Reparameterizable dual-resolution network for real-time semantic segmentation. arXiv preprint arXiv: 2406.12496,.","DOI":"10.2139\/ssrn.5248070"},{"issue":"11","key":"10.1016\/j.eswa.2026.132910_bib0055","doi-asserted-by":"crossref","first-page":"3051","DOI":"10.1007\/s11263-021-01515-2","article-title":"BiSeNet V2: Bilateral network with guided aggregation for real-time semantic segmentation","volume":"129","author":"Yu","year":"2021","journal-title":"International Journal of Computer Vision"},{"key":"10.1016\/j.eswa.2026.132910_bib0056","series-title":"Proceedings of the european conference on computer vision (ECCV)","first-page":"325","article-title":"BiseNet: Bilateral segmentation network for real-time semantic segmentation","author":"Yu","year":"2018"},{"key":"10.1016\/j.eswa.2026.132910_bib0057","series-title":"Proceedings of the computer vision and pattern recognition conference","first-page":"3583","article-title":"2DMamba: Efficient state space model for image representation with applications on giga-pixel whole slide image classification","author":"Zhang","year":"2025"},{"key":"10.1016\/j.eswa.2026.132910_bib0058","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"12083","article-title":"TopFormer: Token pyramid transformer for mobile semantic segmentation","author":"Zhang","year":"2022"},{"key":"10.1016\/j.eswa.2026.132910_bib0059","series-title":"Proceedings of the european conference on computer vision (ECCV)","first-page":"405","article-title":"Icnet for real-time semantic segmentation on high-resolution images","author":"Zhao","year":"2018"},{"key":"10.1016\/j.eswa.2026.132910_bib0060","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"2881","article-title":"Pyramid scene parsing network","author":"Zhao","year":"2017"},{"key":"10.1016\/j.eswa.2026.132910_bib0061","unstructured":"Zhu, L., Liao, B., Zhang, Q., Wang, X., Liu, W., & Wang, X. (2024a). Vision Mamba: Efficient visual representation learning with bidirectional state space model. arXiv preprint arXiv: 2401.09417,."},{"issue":"19","key":"10.1016\/j.eswa.2026.132910_bib0062","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2024.e38495","article-title":"Samba: Semantic segmentation of remotely sensed images with state space model","volume":"10","author":"Zhu","year":"2024","journal-title":"Heliyon"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426018221?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426018221?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T19:33:57Z","timestamp":1783107237000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426018221"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":62,"alternative-id":["S0957417426018221"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132910","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"IMMNet: A real-time semantic segmentation network integrating multi-path Mamba and multi-level local features","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132910","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"132910"}}