Fuzzy - Rough Feature Selection With Π- Membership Function For Mammogram Classification
This work addresses early-stage breast cancer detection for women, but it appears incremental as it builds on existing feature selection and classification methods.
The paper tackles the problem of misidentifying breast cancer in mammograms by proposing a fuzzy-rough feature selection method with a π-membership function, which improves classification accuracy as shown in experimental analysis.
Breast cancer is the second leading cause for death among women and it is diagnosed with the help of mammograms. Oncologists are miserably failed in identifying the micro calcification at the early stage with the help of the mammogram visually. In order to improve the performance of the breast cancer screening, most of the researchers have proposed Computer Aided Diagnosis using image processing. In this study mammograms are preprocessed and features are extracted, then the abnormality is identified through the classification. If all the extracted features are used, most of the cases are misidentified. Hence feature selection procedure is sought. In this paper, Fuzzy-Rough feature selection with π membership function is proposed. The selected features are used to classify the abnormalities with help of Ant-Miner and Weka tools. The experimental analysis shows that the proposed method improves the mammograms classification accuracy.