Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 210800232-9.doi: 10.11896/jsjkx.210800232

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Review on Classification of Breast Cancer Histopathological Images Based on Convolutional Neural Network

ZHANG Xi-ke, MA Zhi-qing, ZHAO Wen-hua, CUI Dong-mei   

  1. College of Intelligence and Information Engineering,Shandong University of Traditional Chinese Medicine,Jinan 250355,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:ZHANG Xi-ke,born in 1997,postgra-duate.His main research interests include medical image processing and analysis.
    MA Zhi-qing,born in 1964,professor,master supervisor.His main research interests include medical image proces-sing and analysis.
  • Supported by:
    Project of Postgraduate Education Quality Improvement Plan in Shandong Province(SDYJG19143),Key Project of Education and Teaching Research in Shandong University of Traditional Chinese Medicine in 2019(ZYZ2019009) and Special Project of Studying and Explaining the Spirit of The Fourth Plenary Session of the 19th CPC Central Committee in Shandong University of Traditional Chinese Medicine(SZQH202009).

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Abstract: Histopathological examination of breast cancer is the “gold standard” for the diagnosis of breast cancer.The classification of breast cancer histopathological images based on convolutional neural network has become one of the research hotspots in the field of medical image processing and analysis.Automatic and accurate classification of breast cancer histopathological images has important clinical application value.Firstly,two public datasets widely used in the classification of breast cancer histopathological images and their evaluation criteria are introduced.Then,the research progress of convolutional neural network on two datasets is mainly elaborated.In the process of describing the research progress,the reasons for the low accuracy of some models are analyzed,and some suggestions are given to improve the performance of the models.Finally,existing problems and future prospects of breast cancer histopathological image classification are discussed.

Key words: Breast cancer, Histopathological image, Image classification, Convolutional neural networks

CLC Number: 

  • TP391
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