精确分类白细胞对于血液恶性肿瘤的诊断尤其是白血病的诊断至关重要。然而，传统的白细胞分类方法耗时且易受检查人员主观解释的影响。为解决这个问题，我们旨在开发一种能够准确分类11种白细胞类型的白细胞分类系统，为放射科医师诊断白血病提供帮助。我们提出的两阶段分类方案涉及基于ResNet的多模型融合的粗略白细胞分类，重点关注形状特征，随后使用支持向量机对基于纹理特征的淋巴细胞进行细微的白细胞分类。我们的数据集包括11,102个11类显微镜下的白细胞图像。我们提出的方法在测试集中实现了高水平的精确白细胞亚型分类，准确性、敏感性、特异性和精确度分别为97.03 ± 0.05、96.76 ± 0.05、99.65 ± 0.05和96.54 ± 0.05。实验结果表明，基于多模型融合的白细胞分类模型可以有效分类11种白细胞类型，为提高血液学分析仪的性能提供有价值的技术支持。©2023年国际医学生物工程联合会。

Accurate classification of leukocytes is crucial for the diagnosis of hematologic malignancies, particularly leukemia. However, traditional leukocyte classification methods are time-consuming and subject to subjective interpretation by examiners. To address this issue, we aimed to develop a leukocyte classification system capable of accurately classifying 11 leukocyte classes, which would aid radiologists in diagnosing leukemia. Our proposed two-stage classification scheme involved a multi-model fusion based on ResNet for rough leukocyte classification, which focused on shape features, followed by fine-grained leukocyte classification using support vector machine for lymphocytes based on texture features. Our dataset consisted of 11,102 microscopic leukocyte images of 11 classes. Our proposed method achieved accurate leukocyte subtype classification with high levels of accuracy, sensitivity, specificity, and precision of 97.03 ± 0.05, 96.76 ± 0.05, 99.65 ± 0.05, and 96.54 ± 0.05, respectively, in the test set. The experimental results demonstrate that the leukocyte classification model based on multi-model fusion can effectively classify 11 leukocyte classes, providing valuable technical support for enhancing the performance of hematology analyzers.© 2023. International Federation for Medical and Biological Engineering.