  The incidence and mortality rates of malignant tumors, such as acute
leukemia, have risen significantly. Clinically, hospitals rely on cytological
examination of peripheral blood and bone marrow smears to diagnose malignant
tumors, with accurate blood cell counting being crucial. Existing automated
methods face challenges such as low feature expression capability, poor
interpretability, and redundant feature extraction when processing
high-dimensional microimage data. We propose a novel fine-grained
classification model, SCKansformer, for bone marrow blood cells, which
addresses these challenges and enhances classification accuracy and efficiency.
The model integrates the Kansformer Encoder, SCConv Encoder, and Global-Local
Attention Encoder. The Kansformer Encoder replaces the traditional MLP layer
with the KAN, improving nonlinear feature representation and interpretability.
The SCConv Encoder, with its Spatial and Channel Reconstruction Units, enhances
feature representation and reduces redundancy. The Global-Local Attention
Encoder combines Multi-head Self-Attention with a Local Part module to capture
both global and local features. We validated our model using the Bone Marrow
Blood Cell Fine-Grained Classification Dataset (BMCD-FGCD), comprising over
10,000 samples and nearly 40 classifications, developed with a partner
hospital. Comparative experiments on our private dataset, as well as the
publicly available PBC and ALL-IDB datasets, demonstrate that SCKansformer
outperforms both typical and advanced microcell classification methods across
all datasets. Our source code and private BMCD-FGCD dataset are available at
https://github.com/JustlfC03/SCKansformer.
