{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T12:30:52Z","timestamp":1773232252524,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T00:00:00Z","timestamp":1773100800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T00:00:00Z","timestamp":1773100800000},"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 Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-026-01885-4","type":"journal-article","created":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T15:49:39Z","timestamp":1773157779000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MDE-YOLO: Edge-Aware Multi-Scale Fusion Lightweight High-Precision Model for Cervical Cell Detection"],"prefix":"10.1007","author":[{"given":"Yang","family":"Fu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong Hong","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,10]]},"reference":[{"key":"1885_CR1","doi-asserted-by":"publisher","unstructured":"Arbyn M, Weiderpass E, Bruni L, et al (2020) Estimates of incidence and mortality of cervical cancer in 2018: a worldwide analysis. Lancet Glob Health 8(2):e191\u2013e203. https:\/\/doi.org\/10.1016\/S2214-109X(19)30482-6","DOI":"10.1016\/S2214-109X(19)30482-6"},{"key":"1885_CR2","doi-asserted-by":"publisher","unstructured":"Koliopoulos G, Nyaga VN, Santesso N, et al (2017) Cytology versus HPV testing for cervical cancer screening in the general population. Cochrane Database Syst Rev 8:CD008587. https:\/\/doi.org\/10.1002\/14651858.CD008587.pub2","DOI":"10.1002\/14651858.CD008587.pub2"},{"key":"1885_CR3","doi-asserted-by":"publisher","unstructured":"Plissiti ME, Nikou C (2012) Cervical cell classification based exclusively on nucleus features. In: Campilho A, Kamel M (eds) Image Analysis and Recognition (ICIAR 2012). LNCS, vol 7325. Springer, Heidelberg, pp 483\u2013490. https:\/\/doi.org\/10.1007\/978-3-642-31298-4_57","DOI":"10.1007\/978-3-642-31298-4_57"},{"key":"1885_CR4","doi-asserted-by":"publisher","unstructured":"Genctav A, Aksoy S, Onder S (2012) Unsupervised segmentation and classification of cervical cell images. Pattern Recognit 45(12):4151\u20134168. https:\/\/doi.org\/10.1016\/j.patcog.2012.05.006","DOI":"10.1016\/j.patcog.2012.05.006"},{"key":"1885_CR5","doi-asserted-by":"publisher","unstructured":"Zhao LL, Li K, Yin JP (2017) Complete three-phase detection framework for identifying abnormal cervical cells. IET Image Process 11(4):258\u2013265. https:\/\/doi.org\/10.1049\/iet-ipr.2016.0788","DOI":"10.1049\/iet-ipr.2016.0788"},{"key":"1885_CR6","doi-asserted-by":"publisher","unstructured":"Zhou ZH, Jiang Y, Yang YB, Chen SF (2002) Lung cancer cell identification based on artificial neural network ensembles. Artif Intell Med 24(1):25\u201336. https:\/\/doi.org\/10.1016\/S0933-3657(01)00094-X","DOI":"10.1016\/S0933-3657(01)00094-X"},{"key":"1885_CR7","doi-asserted-by":"publisher","unstructured":"Kermany DS, Goldbaum M, Cai W, et al (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5):1122\u20131131.e9. https:\/\/doi.org\/10.1016\/j.cell.2018.02.010","DOI":"10.1016\/j.cell.2018.02.010"},{"key":"1885_CR8","doi-asserted-by":"crossref","unstructured":"Yusoff IA, Isa NAM, Othman NH (2010) Performance of neural network architectures: cascaded MLP versus extreme learning machine on cervical cell image classification. In: Proc Int Conf Inf Sci Signal Process Their Appl (ISSPA). IEEE, Kuala Lumpur, Malaysia, pp 308\u2013311.","DOI":"10.1109\/ISSPA.2010.5605463"},{"key":"1885_CR9","unstructured":"Li Z (2018) Cervical cancer cell detection based on strong feature CNN-SVM. PhD dissertation, Beijing Jiaotong University, Beijing, China."},{"key":"1885_CR10","unstructured":"Dong N, Zhao L, Chang J, et al (2019) Feature selection and classification algorithm study of cervical cell images. J Hunan Univ (Nat Sci Ed) 46(12):1\u20138."},{"key":"1885_CR11","doi-asserted-by":"publisher","unstructured":"Kurita Y, Meguro S, Kosugi I, et al (2024) Enhancing cervical cancer cytology screening via artificial intelligence innovation. Sci Rep 14:19535. https:\/\/doi.org\/10.1038\/s41598-024-70670-6","DOI":"10.1038\/s41598-024-70670-6"},{"key":"1885_CR12","doi-asserted-by":"publisher","unstructured":"Xue P, Dang L, Kong LH, et al (2025) Deep learning enabled liquid-based cytology model for cervical precancer and cancer detection. Nat Commun 16:3506. https:\/\/doi.org\/10.1038\/s41467-025-58883-3","DOI":"10.1038\/s41467-025-58883-3"},{"key":"1885_CR13","doi-asserted-by":"publisher","unstructured":"Elakkiya R, Teja KSS, Deborah LJ, et al (2021) Imaging based cervical cancer diagnostics using small object detection\u2013generative adversarial networks. Multimed Tools Appl 80:34971\u201334994. https:\/\/doi.org\/10.1007\/s11042-021-10627-3","DOI":"10.1007\/s11042-021-10627-3"},{"key":"1885_CR14","doi-asserted-by":"publisher","unstructured":"Liang Y, Tang Z, Yan M, et al (2021) Comparison detector for cervical cell\/clumps detection in the limited data scenario. Neurocomputing 437:195\u2013206. https:\/\/doi.org\/10.1016\/j.neucom.2021.01.006","DOI":"10.1016\/j.neucom.2021.01.006"},{"key":"1885_CR15","doi-asserted-by":"publisher","unstructured":"Wang H, Jiang C, Bao K, Xu C (2019) Recognition and clinical diagnosis of cervical cancer cells based on our improved lightweight deep network for pathological image. J Med Syst 43:301. https:\/\/doi.org\/10.1007\/s10916-019-1426-y","DOI":"10.1007\/s10916-019-1426-y"},{"key":"1885_CR16","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proc IEEE Conf Comput Vis Pattern Recognit (CVPR). IEEE, Las Vegas, NV, USA, pp 779\u2013788.","DOI":"10.1109\/CVPR.2016.91"},{"key":"1885_CR17","doi-asserted-by":"publisher","unstructured":"Xiang Y, Sun W, Pan C, et al (2020) A novel automation-assisted cervical cancer reading method based on convolutional neural network. Biocybern Biomed Eng 40(2):611\u2013623. https:\/\/doi.org\/10.1016\/j.bbe.2020.01.016","DOI":"10.1016\/j.bbe.2020.01.016"},{"key":"1885_CR18","unstructured":"Li CW, Yang XN, Zhao SQ, et al (2024) SER-DC YOLO for the detection of abnormal cervical cells. J Harbin Univ Sci Technol 29(1):115\u2013123."},{"key":"1885_CR19","doi-asserted-by":"publisher","unstructured":"Peng Z, Hu R, Wang F, et al (2022) Deep adaptively feature extracting network for cervical squamous lesion cell detection. In: Xu Y, Yan H, Teng H, et al (eds) Machine Learning for Cyber Security (ML4CS 2022). LNCS. Springer, Cham, pp 238\u2013253. https:\/\/doi.org\/10.1007\/978-3-031-20099-1_20","DOI":"10.1007\/978-3-031-20099-1_20"},{"key":"1885_CR20","doi-asserted-by":"crossref","unstructured":"Shi C, Pan Q, Rehman M (2022) Cervical cancer cell image detection method based on improved YOLOv4. In: Proc 7th Int Conf Intell Comput Signal Process (ICSP). IEEE, Lanzhou, China, pp 1996\u20132000.","DOI":"10.1109\/ICSP54964.2022.9778577"},{"key":"1885_CR21","doi-asserted-by":"publisher","unstructured":"Wu N, Jia D, Zhang C, Li Z (2023) Cervical cell extraction network based on optimized YOLO. Math Biosci Eng 20(2):2364\u20132381. https:\/\/doi.org\/10.3934\/mbe.2023111","DOI":"10.3934\/mbe.2023111"},{"key":"1885_CR22","unstructured":"Hu W, Fu R (2024) Trans-YOLOv5: a YOLOv5-based prior transformer network model for automated detection of abnormal cells or clumps in cervical cytology images. Nan Fang Yi Ke Da Xue Xue Bao 44(7):1217\u20131226."},{"key":"1885_CR23","doi-asserted-by":"publisher","unstructured":"Tan M, Pang R, Le QV (2020) EfficientDet: scalable and efficient object detection. In: Proc IEEE\/CVF Conf Comput Vis Pattern Recognit (CVPR). IEEE\/CVF, pp 10781\u201310790. https:\/\/doi.org\/10.1109\/CVPR42600.2020.01079","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"1885_CR24","doi-asserted-by":"crossref","unstructured":"Rahman MM, Munir M, Marculescu R (2024) EMCAD: efficient multi-scale convolutional attention decoding for medical image segmentation. In: Proc IEEE\/CVF Conf Comput Vis Pattern Recognit (CVPR). IEEE\/CVF, pp 11769\u201311779.","DOI":"10.1109\/CVPR52733.2024.01118"},{"key":"1885_CR25","doi-asserted-by":"crossref","unstructured":"Chen Z, He Z, Lu ZM (2023) DEA-Net: single image dehazing based on detail-enhanced convolution and content-guided attention. arXiv:2301.04805.","DOI":"10.1109\/TIP.2024.3354108"},{"key":"1885_CR26","doi-asserted-by":"publisher","unstructured":"Wu Y, He K (2018) Group normalization. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) Computer Vision \u2013 ECCV 2018. LNCS, vol 11217. Springer, Cham, pp 3\u201319. https:\/\/doi.org\/10.1007\/978-3-030-01261-8_1","DOI":"10.1007\/978-3-030-01261-8_1"},{"key":"1885_CR27","unstructured":"Sobel I, Feldman G (1968) A 3\u00d73 isotropic gradient operator for image processing. Stanford Artificial Intelligence Project, Tech Rep AI-223."},{"key":"1885_CR28","doi-asserted-by":"publisher","unstructured":".Jocher G (2020) YOLOv5 by Ultralytics. Zenodo. https:\/\/doi.org\/10.5281\/zenodo.3908559 (accessed 25 Jan 2026).","DOI":"10.5281\/zenodo.3908559"},{"key":"1885_CR29","unstructured":"Li C, Li L, Jiang H, et al (2022) YOLOv6: a single-stage object detection framework for industrial applications. arXiv:2209.02976."},{"key":"1885_CR30","doi-asserted-by":"crossref","unstructured":"Wang CY, Bochkovskiy A, Liao HYM (2023) YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proc IEEE\/CVF Conf Comput Vis Pattern Recognit (CVPR). IEEE\/CVF, pp 7464\u20137475.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"1885_CR31","unstructured":"Jocher G, Chaurasia A, Qiu J (2023) Ultralytics YOLO. https:\/\/github.com\/ultralytics\/ultralytics (accessed 25 Jan 2026)."},{"key":"1885_CR32","doi-asserted-by":"crossref","unstructured":"Wang A, Chen H, Liu L, Chen K, Lin Z, Han J (2024) YOLOv10: real-time end-to-end object detection. arXiv:2405.14458.","DOI":"10.52202\/079017-3429"},{"key":"1885_CR33","unstructured":"Khanam R, Hussain M (2024) YOLOv11: an overview of the key architectural enhancements. arXiv:2410.17725."},{"key":"1885_CR34","unstructured":"Tian Y, Ye Q, Doermann D (2025) YOLOv12: attention-centric real-time object detectors. arXiv:2502.13624."},{"key":"1885_CR35","unstructured":"Lei M, Li S, Wu Y, et al (2025) YOLOv13: real-time object detection with hypergraph-enhanced adaptive visual perception. arXiv:2506.17733."},{"key":"1885_CR36","unstructured":"Zhu X, Su W, Lu L, Li B, Wang X, Dai J (2020) Deformable DETR: deformable transformers for end-to-end object detection. arXiv:2010.04159."},{"key":"1885_CR37","doi-asserted-by":"crossref","unstructured":"Liu S, Qi L, Qin H, Shi J, Jia J (2018) Path aggregation network for instance segmentation. In: Proc IEEE Conf Comput Vis Pattern Recognit (CVPR). IEEE, pp 8759\u20138768.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"1885_CR38","doi-asserted-by":"crossref","unstructured":"Zhang X, Zhou X, Lin M, Sun J (2018) ShuffleNet: an extremely efficient convolutional neural network for mobile devices. In: Proc IEEE Conf Comput Vis Pattern Recognit (CVPR). IEEE, Salt Lake City, UT, USA, pp 6848\u20136856.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"1885_CR39","unstructured":"Roboflow (2025) Cervix Cell Detection (Version 2) . ta-1 workspace. https:\/\/universe.roboflow.com\/ta-1\/cervix-cell-detection\/dataset\/2 (accessed 25 Jan 2026)."},{"key":"1885_CR40","doi-asserted-by":"crossref","unstructured":"Wang CY, Liao HYM (2024) YOLOv9: learning what you want to learn using programmable gradient information. arXiv:2402.13616.","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"1885_CR41","unstructured":"Feng Y, Huang J, Du S, et al (2024) Hyper-YOLO: when visual object detection meets hypergraph computation. arXiv:2408.04804."},{"key":"1885_CR42","doi-asserted-by":"publisher","unstructured":"Wang Z, Li C, Xu H, Zhu X, Li H (2025) Mamba-YOLO: a simple baseline for object detection with state space model. Proc AAAI Conf Artif Intell 39(8):8205\u20138213. https:\/\/doi.org\/10.1609\/aaai.v39i8.32885","DOI":"10.1609\/aaai.v39i8.32885"},{"key":"1885_CR43","doi-asserted-by":"crossref","unstructured":"Zhao Y, Lv W, Xu S, et al (2024) RT-DETR: DETRs beat YOLOs on real-time object detection. In: Proc IEEE\/CVF Conf Comput Vis Pattern Recognit (CVPR). IEEE\/CVF, pp 5501\u20135510.","DOI":"10.1109\/CVPR52733.2024.01605"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-01885-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-026-01885-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-01885-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T15:49:50Z","timestamp":1773157790000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-026-01885-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,10]]},"references-count":43,"alternative-id":["1885"],"URL":"https:\/\/doi.org\/10.1007\/s10278-026-01885-4","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,10]]},"assertion":[{"value":"29 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 January 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study utilized publicly available, de-identified cervical cell image datasets for the development and validation of the MDE-YOLO algorithm. All data sources comply with ethical guidelines for medical image research and data sharing.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"All datasets used in this study contain only de-identified medical images. No patient-identifiable information was accessed, processed, or stored during this research. All images were previously processed to remove any metadata that could potentially identify patients. The research methodology ensures complete anonymization throughout the analysis pipeline.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Consent to Participate"}},{"value":"Not required for this computational research as no direct human subjects were involved. All datasets are used in accordance with their respective licenses and terms of use.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Consent to Publish"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Competing interests"}}]}}