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Survey on Mitosis Detection for Aggressive Breast Cancer from Histological Images
, O. Hujran, K.P. Nitha
Published in Institute of Electrical and Electronics Engineers Inc.
Pages: 232 - 236
The mitotic count is a relevant factor for grading invasive breast cancer. Since it is subject to human prone error, requires more time for completion and the nuclei look similar during all stages of mitosis, automatic detection of mitosis is a good solution to overcome these problems. In this paper, the top methodologies used for mitosis detection are analyzed. Some of them were a part of challenging competitions conducted worldwide. Analysis of the result shows that top approaches, either implemented Random Forest (RF) classifier exploiting intensity feature or used deep learning methods like Convolutional Neural Network (CNN) to give out the best results. It was also found that the ensemble classifiers gives better performance. A preliminary experiment conducted on cascaded RF and Artificial Neural Network (ANN) results in better accuracy than individual classifiers. © 2019 IEEE.
About the journal
JournalData powered by Typeset5th International Conference on Information Management, ICIM 2019
PublisherData powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
Open AccessNo
Concepts (4)
  •  related image
    Artificial neural network
  •  related image
    Breast cancer detection
  •  related image
    Mitosis detection
  •  related image
    Random forest classifier