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Author:

Li, Boya (Li, Boya.) | Li, Jianqiang (Li, Jianqiang.) (Scholars:李建强) | Zhu, Zhichao (Zhu, Zhichao.) | Zhao, Linna (Zhao, Linna.) | Cheng, Wenxiu (Cheng, Wenxiu.)

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CPCI-S EI Scopus

Abstract:

Microscopic imaging plays an important role in the biomedical field. Existing deep learning based methods rely on high-quality data. However, there is a lot of noise (such as bubbles and impurities) in the microscopic images of biological samples collected outdoors, which may lead to significant interference in the microscopic objects identification task. To solve this problem, this paper proposes a deep learning based method for microscopic object localization and classification. Firstly, the whole slide image is preprocessed to obtain the microscopic images after preliminary filtering bubbles and impurities. Then, the sensitized pollen grains are located based on the deep learning model to remove the interference of remaining impurities, and the microscopic images of sensitized pollen grains are classified. This method can effectively suppress the interference of noise in microscopic images on object classification and improve the accuracy and reliability of model. The proposed method is verified by experiments based on real data and the results show that the proposed method achieves the highest accuracy compared with other deep learning methods.

Keyword:

localization classification microscopic object deep learning

Author Community:

  • [ 1 ] [Li, Boya]Beijing Univ Technol, Fac Informat Technologyon, Beijing, Peoples R China
  • [ 2 ] [Li, Jianqiang]Beijing Univ Technol, Fac Informat Technologyon, Beijing, Peoples R China
  • [ 3 ] [Zhu, Zhichao]Beijing Univ Technol, Fac Informat Technologyon, Beijing, Peoples R China
  • [ 4 ] [Zhao, Linna]Beijing Univ Technol, Fac Informat Technologyon, Beijing, Peoples R China
  • [ 5 ] [Cheng, Wenxiu]Beijing Univ Technol, Fac Informat Technologyon, Beijing, Peoples R China

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Source :

2022 IEEE 46TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE (COMPSAC 2022)

Year: 2022

Page: 1426-1431

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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