TY - GEN
T1 - Survey on Mitosis Detection for Aggressive Breast Cancer from Histological Images
AU - Hussain, Hanan
AU - Hujran, Omar
AU - Nitha, K. P.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5/14
Y1 - 2019/5/14
N2 - 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.
AB - 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.
KW - Artificial neural network
KW - Breast cancer detection
KW - Mitosis detection
KW - Random forest classifier
UR - http://www.scopus.com/inward/record.url?scp=85066620818&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85066620818&partnerID=8YFLogxK
U2 - 10.1109/INFOMAN.2019.8714696
DO - 10.1109/INFOMAN.2019.8714696
M3 - Conference contribution
AN - SCOPUS:85066620818
T3 - 5th International Conference on Information Management, ICIM 2019
SP - 232
EP - 236
BT - 5th International Conference on Information Management, ICIM 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Information Management, ICIM 2019
Y2 - 24 March 2019 through 27 March 2019
ER -