OPTIMAL USE OF BIRAD DESCRIPTORS FOR BREAST CANCER DETECTION BY EMPLOYING DATA MINING WITH CORRELATION OF BIOPSY OUTCOME

Abstract

Background: Breast cancer (BC) is commonest among cancer patients, required early detection to be cured. Multiple techniques are used for the diagnosis of BC but mammography is a commonly available and effective tool around the world due to its ability to diagnose BC before it becomes palpable, in reward treatment cost also decreases. Computer-aided diagnosis (CAD) is advanced research to support medical practitioners in cancer diagnosis.

Methods: In comparison to the segmentation technique our research has used “extracted BIRAD characteristics information” from the mammograms. A novel data mining approach for the BIRAD lexicon has been researched in connection with the interactive medical experience. A globally recognized dataset (DDSM) has been used. The characteristics extracted from the mammograms are processed by the data mining method to achieve critical ranges for cancer detection. Multiple classification models with peculiar characteristics have developed to detect BC at the curable stage. 

Results: A total of 1863 patients with an average age of 61 years were evaluated, 48% indicated with malignancies, and 52% with benign lesions, proved with truth biopsy. Critical ranges, by the development of multiple CAD tools, showed remarkable improvement in the recall (NB; 26.9%) and precision (CART; 17.6%) along with reduced FP rate (CART; 16.7%).

Conclusion: The proposed method is profitable because of the individuality of CAD with four classification models along with the practitioner’s experience and radiologist’s opinion in the early detection of BC. For avoiding unnecessary biopsies and differentiating benign and malignant tumor the BIRAD features extraction was benevolent.