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Multi-feature fusion CNNs for Drosophila embryo of interest detection

Author

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  • Xu, Qingzhen
  • Wang, Zhoutao
  • Wang, Fengyun
  • Gong, Yongyi

Abstract

In gene expression, high-resolution Drosophila embryonic images contain abundant temporal and spatial information. The Drosophila embryo of interest detection, with high accuracy and rapidity, is an important preprocessing step in the Drosophila embryonic gene expression computation system. In this paper, we proposed a novel multi-feature fusion (MFF) CNNs framework for the Drosophila embryo of interest detection. Considering the great variety of Drosophila embryonic images, the proposed network takes full advantages of multi-level and multi-scale convolutional features by leveraging the deeply-supervised nets and side-output layers. We built a Drosophila Embryonic Dataset, and train our framework with the Dataset. In the experiment, our method yielded satisfactory results, with advantages in terms of high accuracy (94.9% mean F-measure) and efficiency (40 FPS, i.e. Frame per Second). To the best of our knowledge, it is the first attempt to solve this problem with CNNs and achieves good results.

Suggested Citation

  • Xu, Qingzhen & Wang, Zhoutao & Wang, Fengyun & Gong, Yongyi, 2019. "Multi-feature fusion CNNs for Drosophila embryo of interest detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    • Handle: RePEc:eee:phsmap:v:531:y:2019:i:c:s0378437119310404
    DOI: 10.1016/j.physa.2019.121808
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    Cited by:

    1. Attallah, Omneya & Ibrahim, Rania A. & Zakzouk, Nahla E., 2023. "CAD system for inter-turn fault diagnosis of offshore wind turbines via multi-CNNs & feature selection," Renewable Energy, Elsevier, vol. 203(C), pages 870-880.

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