{"title":"NBCDC-YOLOv8: A new framework to improve blood cell detection and classification based on YOLOv8","authors":"Xuan Chen, Linxuan Li, Xiaoyu Liu, Fengjuan Yin, Xue Liu, Xiaoxiao Zhu, Yufeng Wang, Fanbin Meng","doi":"10.1049/cvi2.12341","DOIUrl":null,"url":null,"abstract":"<p>In recent years, computer technology has successfully permeated all areas of medicine and its management, and it now offers doctors an accurate and rapid means of diagnosis. Existing blood cell detection methods suffer from low accuracy, which is caused by the uneven distribution, high density, and mutual occlusion of different blood cell types in blood microscope images, this article introduces NBCDC-YOLOv8: a new framework to improve blood cell detection and classification based on YOLOv8. Our framework innovates on several fronts: it uses Mosaic data augmentation to enrich the dataset and add small targets, incorporates a space to depth convolution (SPD-Conv) tailored for cells that are small and have low resolution, and introduces the Multi-Separated and Enhancement Attention Module (MultiSEAM) to enhance feature map resolution. Additionally, it integrates a bidirectional feature pyramid network (BiFPN) for effective multi-scale feature fusion and includes four detection heads to improve recognition accuracy of various cell sizes, especially small target platelets. Evaluated on the Blood Cell Classification Dataset (BCCD), NBCDC-YOLOv8 obtains a mean average precision (mAP) of 94.7%, and thus surpasses the original YOLOv8n by 2.3%.</p>","PeriodicalId":56304,"journal":{"name":"IET Computer Vision","volume":"19 1","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cvi2.12341","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Computer Vision","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/cvi2.12341","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
In recent years, computer technology has successfully permeated all areas of medicine and its management, and it now offers doctors an accurate and rapid means of diagnosis. Existing blood cell detection methods suffer from low accuracy, which is caused by the uneven distribution, high density, and mutual occlusion of different blood cell types in blood microscope images, this article introduces NBCDC-YOLOv8: a new framework to improve blood cell detection and classification based on YOLOv8. Our framework innovates on several fronts: it uses Mosaic data augmentation to enrich the dataset and add small targets, incorporates a space to depth convolution (SPD-Conv) tailored for cells that are small and have low resolution, and introduces the Multi-Separated and Enhancement Attention Module (MultiSEAM) to enhance feature map resolution. Additionally, it integrates a bidirectional feature pyramid network (BiFPN) for effective multi-scale feature fusion and includes four detection heads to improve recognition accuracy of various cell sizes, especially small target platelets. Evaluated on the Blood Cell Classification Dataset (BCCD), NBCDC-YOLOv8 obtains a mean average precision (mAP) of 94.7%, and thus surpasses the original YOLOv8n by 2.3%.
期刊介绍：
IET Computer Vision seeks original research papers in a wide range of areas of computer vision. The vision of the journal is to publish the highest quality research work that is relevant and topical to the field, but not forgetting those works that aim to introduce new horizons and set the agenda for future avenues of research in computer vision.
IET Computer Vision welcomes submissions on the following topics:
Biologically and perceptually motivated approaches to low level vision (feature detection, etc.);
Perceptual grouping and organisation
Representation, analysis and matching of 2D and 3D shape
Shape-from-X
Object recognition
Image understanding
Learning with visual inputs
Motion analysis and object tracking
Multiview scene analysis
Cognitive approaches in low, mid and high level vision
Control in visual systems
Colour, reflectance and light
Statistical and probabilistic models
Face and gesture
Surveillance
Biometrics and security
Robotics
Vehicle guidance
Automatic model aquisition
Medical image analysis and understanding
Aerial scene analysis and remote sensing
Deep learning models in computer vision
Both methodological and applications orientated papers are welcome.
Manuscripts submitted are expected to include a detailed and analytical review of the literature and state-of-the-art exposition of the original proposed research and its methodology, its thorough experimental evaluation, and last but not least, comparative evaluation against relevant and state-of-the-art methods. Submissions not abiding by these minimum requirements may be returned to authors without being sent to review.
Special Issues Current Call for Papers:
Computer Vision for Smart Cameras and Camera Networks - https://digital-library.theiet.org/files/IET_CVI_SC.pdf
Computer Vision for the Creative Industries - https://digital-library.theiet.org/files/IET_CVI_CVCI.pdf
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
