2012/05/19 by K. Thangavel, Thangavel, K., R. Roselin +1 · 1 citation
Computer Science · #AI in cancer detection #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Text and Document Classification Technologies #cs.CV
paper · pdf · doi:10.48550/arxiv.1205.4336
Due to Crucial Error
openalex publication_date 2012/05/19 · arxiv created 2012/05/24 · arxiv updated 2012/05/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.