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Delogu, P., Fantacci, M.E., Kasae, P. and Retico, A. (2007) Characterization of Mammographic Masses Using a Gradient Based Segmentation Algorithm and a Neural Classifier. Computers in and Biology Medicine, 37, 1479-1491.
http://dx.doi.org/10.1016/j.compbiomed.2007.01.009

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.