Breast cancer is the second-most common and leading cause of cancer death among women. A Computer Aided Diagnosis (CAD) system for the classification of microcalcification (tumor) lesions in mammogram images is presented in this work. The process uses tumor characteristics in mammogram images, such as shapes (circle or elliptical), sizes (2mm to 5mm), locations (distance from center) and intens...

The main purpose of the proposed system is to develop the diagnosis breast cancer from mammogram image. Presented system includes Preprocessing on mammogram image and uses wavelet feature extraction to improve sensitivity. The proposed system involves three major steps-Preprocessing, Feature Extraction and Classification. Gabor wavelets based features are extracted from medical mammogram images...

Our aim is to propose a new approach to breast pattern classification that will aid in the development of an automated mammographic density analysis procedure. Breast patterns broadly classify the mammographic density and density distribution of each mammogram in order to provide a framework for assessing the risk of breast cancer according to density. However, basic weaknesses of classifying m...

Little is known about the breast cancer risk factors or mammogram characteristics among Native-American women. Southwestern Native-American women have a low risk of breast cancer and a high risk of diabetes. Our purpose was to determine the prevalence of known clinical risk factors for breast cancer and their association with mammogram density in a sample of Southwestern Native-American women u...

In recent years, the stage determining and classifying the mammogram as Benign or Malignant is somewhat complicated process in the medical research. In the earlier papers many classification techniques, CAD designs and feature extraction methods are used constantly for mammogram classification, and has its own advantages and limitations. To overcome the limitations, in this paper a novel approa...

Masses in the breast can be located in digital mammogram images by computationally analysing various feature statistics from the image. Any algorithm used to analyse digital mammogram images can be both time-consuming and errorprone because many areas of these images appear to have features that are mass-like but not masses. Thus false positives are produced which detract from the effectiveness...

BACKGROUND There are no effective breast cancer education programs targeting Samoan women. We tested the effectiveness of a theory-guided, culturally appropriate breast cancer education program (the intervention) designed to increase mammography use among Samoan women. METHODS This community-based participatory cluster-randomized controlled intervention trial used a parallel two-group design....

In 2013 and 2015 combined, 5.4% of U.S. women aged 50-74 years had never received a mammogram in their lifetime. Foreign-born women were twice as likely as U.S.-born women to have never received a mammogram (9.5% versus 4.7%). Foreign-born women who lived in the United States for <25% of their lifetime were more than twice as likely to have never received a mammogram compared with those who res...

BACKGROUND Major national interventions occurred in the early and mid-1990s to increase mammography screening rates among older women. The current study examined mammography utilization by older women during this period. Relation between mammography utilization and demographic measures and health care-related factors also were examined. METHODS A cross-sectional design examined variations in ...