Article citationsMore>>
Kinoshita, S.K., Marques, P.M.A., Slates, A.F.F., Marana, H.R.C., Ferrari, R.J. and Villela, R.L. (1998) Detection and Characterization of Mammographic Masses by Artificial Neural Network. In: Karssemeijer, N., Thijssen, M., Hendriks, J. and van Erning, L., Eds., Digital Mammography, Springer, Netherlands, 489-490.
http://dx.doi.org/10.1007/978-94-011-5318-8_85
has been cited by the following article:
-
TITLE:
Feature Selection Based on Enhanced Cuckoo Search for Breast Cancer Classification in Mammogram Image
AUTHORS:
M. N. Sudha, S. Selvarajan
KEYWORDS:
Breast Cancer Classification, Feature Extraction, Enhanced Cuckoo Search
JOURNAL NAME:
Circuits and Systems,
Vol.7 No.4,
April
27,
2016
ABSTRACT: Proposed system has been developed to extract the optimal features from the breast tumors using Enhanced Cuckoo Search (ECS) and presented in this paper. The texture feature, intensity histogram feature, radial distance feature and shape features have been extracted and the optimal feature set has been obtained using ECS. The overall accuracy of a minimum distance classifier and k-Nearest Neighbor (k-NN) on validation samples is used as a fitness value for ECS. The new approach is carried out on the extracted feature dataset. The proposed system selects only the minimum number of features and performed the accuracy of 98.75% with Minimum Distance Classifier and 99.13% with k-NN Classifier. The performance of the new ECS is compared with the Cuckoo Search and Harmony Search. This result shows that the ECS algorithm is more accurate than the other algorithm. The proposed system can provide valuable information to the physician in medical pathology.
Related Articles:
-
Hakan Koyuncu, Baki Koyuncu
-
Himanshu Gothwal, Silky Kedawat, Rajesh Kumar
-
Eri Matsuyama, Megumi Takehara, Du-Yih Tsai
-
Hanieh Mohammadi, Meshkat Nemati, Zohreh Allahmoradi, Hoda Forghani Raissi, Somayeh Saraf Esmaili, Ali Sheikhani
-
Vincenzo Pacelli, Vitoantonio Bevilacqua, Michele Azzollini