Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: TE-YOLOF: Tiny and efficient YOLOF for blood cell detection   Blood cell detection in microscopic images is an essential branch of medical
image processing research. Since disease detection based on manual checking of
blood cells is time-consuming and full of errors, testing of blood cells using
object detectors with Deep Convolutional Neural Network can be regarded as a
feasible solution. In this work, an object detector based on YOLOF has been
proposed to detect blood cell objects such as red blood cells, white blood
cells and platelets. This object detector is called TE-YOLOF, Tiny and
Efficient YOLOF, and it is a One-Stage detector using dilated encoder to
extract information from single-level feature maps. For increasing efficiency
and flexibility, the EfficientNet Convolutional Neural Network is utilized as
the backbone for the proposed object detector. Furthermore, the Depthwise
Separable Convolution is applied to enhance the performance and minimize the
parameters of the network. In addition, the Mish activation function is
employed to increase the precision. Extensive experiments on the BCCD dataset
prove the effectiveness of the proposed model, which is more efficient than
other existing studies for blood cell detection.

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The categories of this paper are: supr-con
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The categories of this paper are: dg-ga
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The categories of this paper are: plasm-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
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The categories of this paper are: math-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: eess
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The categories of this paper are: bayes-an
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The categories of this paper are: adap-org
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The categories of this paper are: hep-ph
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The categories of this paper are: q-bio
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The categories of this paper are: gr-qc
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The categories of this paper are: patt-sol
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The categories of this paper are: q-fin