Medical & Biological Engineering & Computing

, Volume 57, Issue 6, pp 1187–1198 | Cite as

Fluorescence microscopy image classification of 2D HeLa cells based on the CapsNet neural network

  • XiaoQing ZhangEmail author
  • Shu-Guang Zhao
Original Article


The development of computer technology now allows the quick and efficient automatic fluorescence microscopy generation of a large number of images of proteins in specific subcellular compartments using fluorescence microscopy. Digital image processing and pattern recognition technology can easily classify these images, identify the subcellular location of proteins, and subsequently carry out related work such as analysis and investigation of protein function. Here, based on a fluorescence microscopy 2D image dataset of HeLa cells, the CapsNet network model was used to classify ten types of images of proteins in different subcellular compartments. Capsules in the CapsNet network model were trained to capture the possibility of certain features and variants rather than to capture the characteristics of a specific variant. The capsule at the same level predicted the instantiation parameters of the higher level capsule through the transformation matrix, and the higher level capsule became active when multiple dynamic routing forecasts were consistent. Experiments show that using the CapsNet network model to classify 2D HeLa datasets can achieve higher accuracy.

Graphical abstract


CapsNet Subcellular localization Fluorescence microscopy 2D HeLa Image classification Neural network 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© International Federation for Medical and Biological Engineering 2019

Authors and Affiliations

  1. 1.College of Information Science and TechnologyDonghua UniversityShanghaiChina
  2. 2.Nanjing University of Chinese Medicine Hanlin CollegeTaizhouChina

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