TY - GEN
T1 - Breast cancer diagnosis using neural-based linear fusion strategies
AU - Wu, Yunfeng
AU - Wang, Cong
AU - Ng, S. C.
AU - Madabhushi, Anant
AU - Zhong, Yixin
PY - 2006
Y1 - 2006
N2 - Breast cancer is one of the leading causes of mortality among women, and the early diagnosis is of significant clinical importance. In this paper, we describe several linear fusion strategies, in particular the Majority Vote, Simple Average, Weighted Average, and Perception Average, which are used to combine a group of component multilayer perceptions with optimal architecture for the classification of breast lesions. In our experiments, we utilize the criteria of mean squared error, absolute classification error, relative error ratio, and Receiver Operating Characteristic (ROC) curve to concretely evaluate and compare the performances of the four fusion strategies. The experimental results demonstrate that the Weighted Average and Perception Average strategies can achieve better diagnostic performance compared to the Majority Vote and Simple Average methods.
AB - Breast cancer is one of the leading causes of mortality among women, and the early diagnosis is of significant clinical importance. In this paper, we describe several linear fusion strategies, in particular the Majority Vote, Simple Average, Weighted Average, and Perception Average, which are used to combine a group of component multilayer perceptions with optimal architecture for the classification of breast lesions. In our experiments, we utilize the criteria of mean squared error, absolute classification error, relative error ratio, and Receiver Operating Characteristic (ROC) curve to concretely evaluate and compare the performances of the four fusion strategies. The experimental results demonstrate that the Weighted Average and Perception Average strategies can achieve better diagnostic performance compared to the Majority Vote and Simple Average methods.
UR - https://www.scopus.com/pages/publications/33750700449
U2 - 10.1007/11893295_19
DO - 10.1007/11893295_19
M3 - Conference contribution
AN - SCOPUS:33750700449
SN - 3540464840
SN - 9783540464846
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 165
EP - 175
BT - Neural Information Processing - 13th International Conference, ICONIP 2006, Proceedings
T2 - 13th International Conference on Neural Information Processing, ICONIP 2006
Y2 - 3 October 2006 through 6 October 2006
ER -
