ABSTRACT
Data analysis based on breast cancer risk factors such as age, race, breastfeeding, hormone replacement therapy, family history, and obesity was conducted on breast cancer patients using a new enhanced computerized database management system. My Structural Query Language (MySQL) is selected as the application for database management system to store the patient data collected from hospitals in Malaysia. An automatic calculation tool is embedded in this system to assist the data analysis. The results are plotted automatically and a user-friendly graphical user interface is developed that can control the MySQL database. Case studies show breast cancer incidence rate is highest among Malay women, followed by Chinese and Indian. The peak age for breast cancer incidence is from 50 to 59 years old. Results suggest that the chance of developing breast cancer is increased in older women, and reduced with breastfeeding practice. The weight status might affect the breast cancer risk differently. Additional studies are needed to confirm these findings.
ABSTRACT
A computer-aided detection auto-probing (CADAP) system is presented for detecting breast lesions using dynamic contrast enhanced magnetic resonance imaging, through a spatial-based discrete Fourier transform. The stand-alone CADAP system reduces noise, refines region of interest (ROI) automatically, and detects the breast lesion with minimal false positive detection. The lesions are then classified and colourised according to their characteristics, whether benign, suspicious or malignant. To enhance the visualisation, the entire analysed ROI is constructed into a 3-D image, so that the user can diagnose based on multiple views on the ROI. The proposed method has been applied to 101 sets of digital images, and the results compared with the biopsy results done by radiologists. The proposed scheme is able to identify breast cancer regions accurately and efficiently.