Abstract:
: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 ᅟ.
journal_name
Med Biol Eng Computjournal_title
Medical & biological engineering & computingauthors
Zhang X,Zhao SGdoi
10.1007/s11517-018-01946-zsubject
Has Abstractpub_date
2019-06-01 00:00:00pages
1187-1198issue
6eissn
0140-0118issn
1741-0444pii
10.1007/s11517-018-01946-zjournal_volume
57pub_type
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