Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Framework based on Deep Neural Networks to Extract Anatomy of
  Mosquitoes from Images   We design a framework based on Mask Region-based Convolutional Neural Network
(Mask R-CNN) to automatically detect and separately extract anatomical
components of mosquitoes - thorax, wings, abdomen and legs from images. Our
training dataset consisted of 1500 smartphone images of nine mosquito species
trapped in Florida. In the proposed technique, the first step is to detect
anatomical components within a mosquito image. Then, we localize and classify
the extracted anatomical components, while simultaneously adding a branch in
the neural network architecture to segment pixels containing only the
anatomical components. Evaluation results are favorable. To evaluate
generality, we test our architecture trained only with mosquito images on
bumblebee images. We again reveal favorable results, particularly in extracting
wings. Our techniques in this paper have practical applications in public
health, taxonomy and citizen-science efforts.

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The categories of this paper are: hep-th
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The categories of this paper are: stat
28
The categories of this paper are: alg-geom
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The categories of this paper are: astro-ph
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The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
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The categories of this paper are: supr-con
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The categories of this paper are: q-bio
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The categories of this paper are: cond-mat
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The categories of this paper are: dg-ga
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The categories of this paper are: comp-gas
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: solv-int