Wang XinZheng, Pan GuangJian, Hu ZhiGang, Ge AoRu
College of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, 471023, China.
Sci Rep. 2025 Mar 11;15(1):8427. doi: 10.1038/s41598-025-91720-7.
Current diagnoses of leukemia are typically performed manually by physicians on the basis of blood cell morphology, leading to challenges such as excessive workload, limited efficiency, and subjective outcomes. To solve the above problems, a two-stage detection method was developed for the automatic detection and identification of blood cells. First, for the blood cell detection task, an improved YOLOv7 blood cell detection model was proposed that integrates multihead attention and the SCYLLA-IoU (SIoU) loss function to accurately locate and classify white blood cells (WBCs), red blood cells (RBCs), and platelets in a full-field image of blood cells. For the white blood cell identification task of detecting network positioning, an improved EfficientNetv2 classification model was subsequently developed, which integrates the atrous spatial pyramid pooling (ASPP) module to increase classification accuracy and employs the balanced cross-entropy (BCE) function to address sample number imbalance. The experiments utilized four publicly accessible datasets: BCCD, LDWBC, LISC, and Raabin. The proposed detection model achieved an average accuracy of 94.7% in detecting and identifying blood cells in the BCCD dataset. With an IoU equal to 0.5, the model attained a mean average precision (mAP) of 97.17%. In the white blood cell classification task, an average precision (AP) of 95.12% and an average recall (AR) of 97% were achieved on the LDWBC, LISC, and Raabin datasets. The experimental results demonstrate that the proposed two-stage detection method detects and identifies blood cells accurately, thereby facilitating automatic detection, classification, and quantification of blood cell images, which can aid doctors in preliminary leukemia diagnosis.
目前白血病的诊断通常由医生根据血细胞形态手动进行，这带来了诸如工作量过大、效率有限和结果主观等挑战。为了解决上述问题，开发了一种用于血细胞自动检测和识别的两阶段检测方法。首先，针对血细胞检测任务，提出了一种改进的YOLOv7血细胞检测模型，该模型集成了多头注意力和SCYLLA-IoU（SIoU）损失函数，以在血细胞全场图像中准确地定位和分类白细胞（WBC）、红细胞（RBC）和血小板。对于检测网络定位的白细胞识别任务，随后开发了一种改进的EfficientNetv2分类模型，该模型集成了空洞空间金字塔池化（ASPP）模块以提高分类准确率，并采用平衡交叉熵（BCE）函数来解决样本数量不平衡问题。实验使用了四个可公开获取的数据集：BCCD、LDWBC、LISC和Raabin。所提出的检测模型在BCCD数据集中检测和识别血细胞时达到了94.7%的平均准确率。在交并比（IoU）等于0.5的情况下，该模型的平均精度均值（mAP）达到了97.17%。在白细胞分类任务中，在LDWBC、LISC和Raabin数据集上实现了95.12%的平均精度（AP）和97%的平均召回率（AR）。实验结果表明，所提出的两阶段检测方法能够准确地检测和识别血细胞，从而有助于血细胞图像的自动检测、分类和定量，这可以辅助医生进行白血病的初步诊断。
