{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T05:14:50Z","timestamp":1740114890765,"version":"3.37.3"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T00:00:00Z","timestamp":1740009600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T00:00:00Z","timestamp":1740009600000},"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":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-025-03769-w","type":"journal-article","created":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T07:10:48Z","timestamp":1740035448000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Efficient Blood Cell Detection with Detr: A Novel Approach for Automated Hematological Image Analysis"],"prefix":"10.1007","volume":"6","author":[{"given":"Lalitha Guru","family":"Swaminathan","sequence":"first","affiliation":[]},{"given":"Jayasooriya","family":"Giridharan","sequence":"additional","affiliation":[]},{"given":"Janhavi","family":"Kulkarni","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5097-6189","authenticated-orcid":false,"given":"Ravi","family":"Visvanathan","sequence":"additional","affiliation":[]},{"given":"S. Sofana","family":"Reka","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,20]]},"reference":[{"issue":"2","key":"3769_CR1","doi-asserted-by":"publisher","first-page":"1813","DOI":"10.1007\/s10462-020-09825-6","volume":"55","author":"A Khan","year":"2022","unstructured":"Khan A, Sohail A, Zahoora U, Qureshi AS. A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev. 2022;55(2):1813\u201355. https:\/\/doi.org\/10.1007\/s10462-020-09825-6.","journal-title":"Artif Intell Rev"},{"key":"3769_CR2","doi-asserted-by":"publisher","unstructured":"Nguyen T, Linh T. Machine learning techniques in healthcare: applications and challenges. J Healthc Eng. 2021;2021. https:\/\/doi.org\/10.1155\/2021\/6673409.","DOI":"10.1155\/2021\/6673409"},{"issue":"10","key":"3769_CR3","doi-asserted-by":"publisher","first-page":"4381","DOI":"10.1007\/s00521-020-05454-2","volume":"33","author":"G Mohan","year":"2021","unstructured":"Mohan G, Subramaniam S. Deep learning models for medical image analysis: advances, challenges, and future directions. Neural Comput Appl. 2021;33(10):4381\u201394. https:\/\/doi.org\/10.1007\/s00521-020-05454-2.","journal-title":"Neural Comput Appl"},{"issue":"9","key":"3769_CR4","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1136\/jclinpath-2021-207698","volume":"75","author":"Y Gao","year":"2022","unstructured":"Gao Y, Zhou J, Jiang Z. Blood cell detection using deep learning and its clinical significance in hematology. J Clin Pathol. 2022;75(9):571\u20137. https:\/\/doi.org\/10.1136\/jclinpath-2021-207698.","journal-title":"J Clin Pathol"},{"key":"3769_CR5","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/s41666-023-00123-1","volume":"7","author":"J Zhu","year":"2023","unstructured":"Zhu J, Fu H, Lin H, Li W. Deep learning methods for medical image analysis: a review of the recent advances and applications. J Healthc Inf Res. 2023;7:123\u201339. https:\/\/doi.org\/10.1007\/s41666-023-00123-1.","journal-title":"J Healthc Inf Res"},{"key":"3769_CR6","doi-asserted-by":"publisher","first-page":"105985","DOI":"10.1016\/j.ijmedinf.2023.105985","volume":"178","author":"P Chakrabarty","year":"2024","unstructured":"Chakrabarty P, Gupta P. Transformers and deep learning for medical diagnostics: current applications and future trends. Int J Med Informatics. 2024;178:105985. https:\/\/doi.org\/10.1016\/j.ijmedinf.2023.105985.","journal-title":"Int J Med Informatics"},{"issue":"2","key":"3769_CR7","doi-asserted-by":"publisher","first-page":"98","DOI":"10.2174\/1573405619666220802111254","volume":"20","author":"N Patel","year":"2024","unstructured":"Patel N, Poudel S, Bajaj V. Emerging transformer-based methods for biomedical imaging. Curr Med Imaging Reviews. 2024;20(2):98\u2013110. https:\/\/doi.org\/10.2174\/1573405619666220802111254.","journal-title":"Curr Med Imaging Reviews"},{"issue":"6","key":"3769_CR8","doi-asserted-by":"publisher","first-page":"1803","DOI":"10.1109\/TPAMI.2021.3058445","volume":"44","author":"K He","year":"2021","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. IEEE Trans Pattern Anal Mach Intell. 2021;44(6):1803\u201322. https:\/\/doi.org\/10.1109\/TPAMI.2021.3058445.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3769_CR9","doi-asserted-by":"publisher","first-page":"105098","DOI":"10.1016\/j.compbiomed.2021.105098","volume":"140","author":"T Xiao","year":"2022","unstructured":"Xiao T, Lu W. Medical image analysis using transformers: a review. Comput Biol Med. 2022;140:105098. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.105098.","journal-title":"Comput Biol Med"},{"issue":"6","key":"3769_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3530819","volume":"55","author":"Y Tay","year":"2022","unstructured":"Tay Y, Dehghani M, Bahri D, Metzler D. Efficient transformers: a survey. ACM-CSUR. 2022;55(6):1\u201328. https:\/\/doi.org\/10.1145\/3530819.","journal-title":"ACM-CSUR"},{"issue":"2","key":"3769_CR11","doi-asserted-by":"publisher","first-page":"744","DOI":"10.1109\/TPAMI.2022.3142212","volume":"45","author":"A Dosovitskiy","year":"2023","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D. An image is worth 16x16 words: Transformers for image recognition at scale. IEEE Trans Pattern Anal Mach Intell. 2023;45(2):744\u201358. https:\/\/doi.org\/10.1109\/TPAMI.2022.3142212.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"3769_CR12","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1109\/TMI.2023.3250298","volume":"43","author":"K Han","year":"2024","unstructured":"Han K, Wang Y, Chen H, Zhang X. Vision transformers in medical image processing: a critical survey. IEEE Trans Med Imaging. 2024;43(3):625\u201339. https:\/\/doi.org\/10.1109\/TMI.2023.3250298.","journal-title":"IEEE Trans Med Imaging"},{"key":"3769_CR13","doi-asserted-by":"publisher","first-page":"104091","DOI":"10.1016\/j.jbi.2023.104091","volume":"138","author":"A Vaswani","year":"2023","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Gomez A. Attention is all you need: revisited for blood cell detection. J Biomed Inform. 2023;138:104091. https:\/\/doi.org\/10.1016\/j.jbi.2023.104091.","journal-title":"J Biomed Inform"},{"key":"3769_CR14","doi-asserted-by":"publisher","first-page":"102487","DOI":"10.1016\/j.artmed.2024.102487","volume":"135","author":"X Zhou","year":"2024","unstructured":"Zhou X, Li M, Luo W. Advances in artificial intelligence for blood cell detection: a review of transformer-based approaches. Artif Intell Med. 2024;135:102487. https:\/\/doi.org\/10.1016\/j.artmed.2024.102487.","journal-title":"Artif Intell Med"},{"key":"3769_CR15","doi-asserted-by":"publisher","first-page":"109812","DOI":"10.1016\/j.patcog.2023.109812","volume":"144","author":"J Li","year":"2023","unstructured":"Li J, Li L, Li Q, Duan L. Automated blood cell classification using deep convolutional neural networks: a survey. Pattern Recogn. 2023;144:109812. https:\/\/doi.org\/10.1016\/j.patcog.2023.109812.","journal-title":"Pattern Recogn"},{"key":"3769_CR16","doi-asserted-by":"publisher","unstructured":"Dhieb N, Ghazzai H, Besbes H, Massoud Y. An automated blood cells counting and classification Framework using Mask R-CNN Deep Learning Model. 2019;300\u20133. https:\/\/doi.org\/10.1109\/ICM48031.2019.9021862","DOI":"10.1109\/ICM48031.2019.9021862"},{"key":"3769_CR17","doi-asserted-by":"publisher","unstructured":"Talaat A, Kollmannsberger P, Ewees A. Efficient classification of White Blood Cell Leukemia with Improved Swarm optimization of Deep Features. Sci Rep. 2020;10. https:\/\/doi.org\/10.1038\/s41598-020-59215-9.","DOI":"10.1038\/s41598-020-59215-9"},{"key":"3769_CR18","doi-asserted-by":"publisher","unstructured":"Siddique MAI, Bin Aziz A, Zahid, Matin A. An improved deep learning-based classification of human white blood cell images. 2020. https:\/\/doi.org\/10.1109\/ICECE51571.2020.9393156","DOI":"10.1109\/ICECE51571.2020.9393156"},{"key":"3769_CR19","doi-asserted-by":"publisher","first-page":"102385","DOI":"10.1016\/j.bspc.2020.102385","volume":"65","author":"RM Roy","year":"2021","unstructured":"Roy RM, Ameer PM. Segmentation of leukocyte by semantic segmentation model: a deep learning approach. Biomed Signal Process Control. 2021;65:102385. https:\/\/doi.org\/10.1016\/j.bspc.2020.102385.","journal-title":"Biomed Signal Process Control"},{"key":"3769_CR20","unstructured":"Gavas E, Olpadkar K. (2021). Deep CNNs for Peripheral Blood Cell Classification."},{"key":"3769_CR21","doi-asserted-by":"publisher","unstructured":"Muhamad HA, Kareem SW, Mohammed AS. A Comparative Evaluation of Deep Learning Methods in Automated Classification of White Blood Cell Images. 2022 8th International Engineering Conference on Sustainable Technology and Development (IEC), Erbil, Iraq, 2022, pp. 205\u2013211. https:\/\/doi.org\/10.1109\/IEC54822.2022.9807456","DOI":"10.1109\/IEC54822.2022.9807456"},{"key":"3769_CR22","doi-asserted-by":"publisher","unstructured":"Bayat N, Davey D, Coathup M, Park J-H. White Blood Cell Classification Using Multi-Attention Data Augmentation and Regularization. Big Data and Cognitive Computing. 2022;6(122). https:\/\/doi.org\/10.3390\/bdcc6040122","DOI":"10.3390\/bdcc6040122"},{"key":"3769_CR23","doi-asserted-by":"publisher","unstructured":"Chen H, Liu J, Hua C, Feng J, Pang, Baochuan, Cao, Dehua, Li C. Accurate classification of white blood cells by coupling pre-trained ResNet and DenseNet with SCAM mechanism. BMC Bioinformatics. 2022;23. https:\/\/doi.org\/10.1186\/s12859-022-04824-6.","DOI":"10.1186\/s12859-022-04824-6"},{"key":"3769_CR24","doi-asserted-by":"publisher","unstructured":"Ban C-G, Park D, Hwang Y. Image classification using DETR based object-level feature. 22nd International Conference on Control, Automation and Systems (ICCAS). 2022 https:\/\/doi.org\/10.23919\/iccas55662.2022.10003912","DOI":"10.23919\/iccas55662.2022.10003912"},{"key":"3769_CR25","unstructured":"Pal et al. (2023). Introduced HemaX, an explainable deep learning system for WBC classification."},{"key":"3769_CR26","unstructured":"Li, Dongming & Tang, Peng & Zhang, Run & Sun, Changming & Yong, Li & Qian, Jingning & Liang, Yan & Yang, Jinhua & Zhang, Lijuan. (2023). Robust Blood Cell Image Segmentation Method Based on Neural Ordinary Differential Equations. Computational and Mathematical Methods in Medicine."},{"issue":"1","key":"3769_CR27","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1080\/21691401.2021.1879823","volume":"49","author":"X Yao","year":"2021","unstructured":"Yao X, Sun K, Bu X, Zhao C, Jin Y. Classification of white blood cells using weighted optimized deformable convolutional neural networks. Artif Cells Nanomed Biotechnol. 2021;49(1):147\u201355.","journal-title":"Artif Cells Nanomed Biotechnol"},{"key":"3769_CR28","doi-asserted-by":"crossref","unstructured":"Deutges, Michael, Ario Sadafi, Nassir Navab, and Carsten Marr. Neural Cellular Automata for Lightweight, Robust and Explainable Classification of White Blood Cell Images. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 693\u2013702. Cham: Springer Nature Switzerland, 2024.","DOI":"10.1007\/978-3-031-72384-1_65"},{"key":"3769_CR29","unstructured":"Chen, Yuzhuo, Zetong Chen, Yunuo An, Chenyang Lu, and Xu Qiao. DAFFNet: A Dual Attention Feature FusionNetwork for Classification of White Blood Cells. arXiv preprint arXiv:2405.16220 (2024)."},{"key":"3769_CR30","unstructured":"Kim and Kim. Evaluated modern computer vision models like MaxVit, EfficientNet, and MobileNetV3 for WBC classification. 2024"},{"key":"3769_CR31","first-page":"108711","volume":"108","author":"FIF Escobar","year":"2024","unstructured":"Escobar FIF, Jacqueline Rose T, Alipo-on, Jemima Louise U, Novia, Myles Joshua T, Tan. Hezerul Abdul Karim, and Nouar AlDahoul. Understanding differences in applying DETR to natural and medical images. Comput Electr Eng. 2024;108:108711.","journal-title":"Comput Electr Eng"},{"key":"3769_CR32","doi-asserted-by":"crossref","first-page":"108712","DOI":"10.1016\/j.compeleceng.2023.108710","volume":"108","author":"F Escobar","year":"2023","unstructured":"Escobar F, Isabelle F, Jacqueline Rose T, Alipo-on J, Louise U, Novia, Myles Joshua T, Tan. Hezerul Abdul Karim, and Nouar AlDahoul. A Comprehensive Review of YOLO Architectures in Computer Vision: from YOLOv1 to YOLOv9. Comput Electr Eng. 2023;108:108712.","journal-title":"Comput Electr Eng"},{"key":"3769_CR33","first-page":"108713","volume":"108","author":"F Escobar","year":"2024","unstructured":"Escobar F, Isabelle F, Jacqueline Rose T, Alipo-on, Jemima Louise U, Novia, Myles Joshua T, Tan. Hezerul Abdul Karim, and Nouar AlDahoul. What is YOLOv5: a deep look into the internal features of the popular object detector. Comput Electr Eng. 2024;108:108713.","journal-title":"Comput Electr Eng"},{"key":"3769_CR34","doi-asserted-by":"crossref","first-page":"108714","DOI":"10.1016\/j.compeleceng.2023.108714","volume":"108","author":"FIF Escobar","year":"2023","unstructured":"Escobar FIF, Jacqueline Rose T, Alipo-on J, Louise U, Novia, Myles Joshua T, Tan. Hezerul Abdul Karim, and Nouar AlDahoul. A comprehensive review of YOLOv5: advances in real-time object detection. Comput Electr Eng. 2023;108:108714.","journal-title":"Comput Electr Eng"},{"key":"3769_CR35","first-page":"108715","volume":"108","author":"FIF Escobar","year":"2024","unstructured":"Escobar FIF, Jacqueline Rose T, Alipo-on J, Louise U, Novia, Myles Joshua T, Tan. Hezerul Abdul Karim, and Nouar AlDahoul. Real-time medical lesion screening: accurate and rapid detectors. Comput Electr Eng. 2024;108:108715.","journal-title":"Comput Electr Eng"},{"key":"3769_CR36","unstructured":"Ammar Nassan Alhajali. BCCD (COCO). Kaggle, 2021. https:\/\/www.kaggle.com\/datasets\/ammarnassanalhajali\/bccd-coco"},{"key":"3769_CR37","unstructured":"Shenggan. BCCD_Dataset. GitHub, 2018. https:\/\/github.com\/Shenggan\/BCCD_Dataset"},{"key":"3769_CR38","unstructured":"Jeet Blahiri. BCCD Dataset with Mask. Kaggle, 2020. https:\/\/www.kaggle.com\/datasets\/jeetblahiri\/bccd-dataset-with-mask"},{"key":"3769_CR39","unstructured":"BCCD Dataset. Papers Code, 2020. https:\/\/paperswithcode.com\/dataset\/bccd"},{"issue":"4","key":"3769_CR40","first-page":"99","volume":"57","author":"X Zhao","year":"2024","unstructured":"Zhao X, Wang L, Zhang Y, Han X, Deveci M, Parmar M. Rev Convolutional Neural Networks Comput Vis Artif Intell Rev. 2024;57(4):99.","journal-title":"Rev Convolutional Neural Networks Comput Vis Artif Intell Rev"},{"key":"3769_CR41","doi-asserted-by":"crossref","unstructured":"Iwana B, Kenji R, Kuroki, Uchida S. Explaining convolutional neural networks using softmax gradient layer-wise relevance propagation. In 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), 2019:4176\u20134185. IEEE.","DOI":"10.1109\/ICCVW.2019.00513"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-03769-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-025-03769-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-03769-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T07:11:00Z","timestamp":1740035460000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-025-03769-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,20]]},"references-count":41,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["3769"],"URL":"https:\/\/doi.org\/10.1007\/s42979-025-03769-w","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,20]]},"assertion":[{"value":"11 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 February 2025","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 have no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"202"}}