  The precise categorization of white blood cell (WBC) is crucial for
diagnosing blood-related disorders. However, manual analysis in clinical
settings is time-consuming, labor-intensive, and prone to errors. Numerous
studies have employed machine learning and deep learning techniques to achieve
objective WBC classification, yet these studies have not fully utilized the
information of WBC images. Therefore, our motivation is to comprehensively
utilize the morphological information and high-level semantic information of
WBC images to achieve accurate classification of WBC. In this study, we propose
a novel dual-branch network Dual Attention Feature Fusion Network (DAFFNet),
which for the first time integrates the high-level semantic features with
morphological features of WBC to achieve accurate classification. Specifically,
we introduce a dual attention mechanism, which enables the model to utilize the
channel features and spatially localized features of the image more
comprehensively. Morphological Feature Extractor (MFE), comprising
Morphological Attributes Predictor (MAP) and Morphological Attributes Encoder
(MAE), is proposed to extract the morphological features of WBC. We also
implement Deep-supervised Learning (DSL) and Semi-supervised Learning (SSL)
training strategies for MAE to enhance its performance. Our proposed network
framework achieves 98.77%, 91.30%, 98.36%, 99.71%, 98.45%, and 98.85% overall
accuracy on the six public datasets PBC, LISC, Raabin-WBC, BCCD, LDWBC, and
Labelled, respectively, demonstrating superior effectiveness compared to
existing studies. The results indicate that the WBC classification combining
high-level semantic features and low-level morphological features is of great
significance, which lays the foundation for objective and accurate
classification of WBC in microscopic blood cell images.
