  In standard hospital blood tests, the traditional process requires doctors to
manually isolate leukocytes from microscopic images of patients' blood using
microscopes. These isolated leukocytes are then categorized via automatic
leukocyte classifiers to determine the proportion and volume of different types
of leukocytes present in the blood samples, aiding disease diagnosis. This
methodology is not only time-consuming and labor-intensive, but it also has a
high propensity for errors due to factors such as image quality and
environmental conditions, which could potentially lead to incorrect subsequent
classifications and misdiagnosis. To address these issues, this paper proposes
an innovative method of leukocyte detection: the Multi-level Feature Fusion and
Deformable Self-attention DETR (MFDS-DETR). To tackle the issue of leukocyte
scale disparity, we designed the High-level Screening-feature Fusion Pyramid
(HS-FPN), enabling multi-level fusion. This model uses high-level features as
weights to filter low-level feature information via a channel attention module
and then merges the screened information with the high-level features, thus
enhancing the model's feature expression capability. Further, we address the
issue of leukocyte feature scarcity by incorporating a multi-scale deformable
self-attention module in the encoder and using the self-attention and
cross-deformable attention mechanisms in the decoder, which aids in the
extraction of the global features of the leukocyte feature maps. The
effectiveness, superiority, and generalizability of the proposed MFDS-DETR
method are confirmed through comparisons with other cutting-edge leukocyte
detection models using the private WBCDD, public LISC and BCCD datasets. Our
source code and private WBCCD dataset are available at
https://github.com/JustlfC03/MFDS-DETR.
