Handbook of pattern recognition & computer vision
ACM Computing Surveys (CSUR)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
An efficient SVM-GA feature selection model for large healthcare databases
Proceedings of the 10th annual conference on Genetic and evolutionary computation
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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Breast cancer is one of the major causes of death among women all over the world. Presently, mammographic analysis is the most used method for early detection of abnormalities. This paper presents a computational methodology to help the specialist with this task. In the first step, the K-Means clustering algorithm and the Template Matching technique are used to detect suspicious regions. Next, the texture of each region is described using the Simpson's Diversity Index, which is used in Ecology to measure the biodiversity of an ecosystem. Finally, the information of texture is used by SVM to classify the suspicious regions into two classes: masses and non-masses. The tests demonstrate that the methodology has 79.12% of accuracy, 77.27% of sensitivity, and 79.66% of specificity.