Sun, Yajie. Ph.D., Purdue University, August, 2004. Normal Mammogram Analysis. Major Professor: Edward J. Delp. Breast cancer is the leading cause of cancer death among women. Screening mammography is the only method currently available for the reliable detection of early and potentially curable breast cancer. Research indicates that the mortality rate could decrease by 30% if women age 50 and ...

Breast cancer is one of the most common types of cancer in women. Screening mammography is a low‑dose X‑ray examination of breasts, which is conducted to detect breast cancer at early stages when the cancerous tumor is too small to be felt as a lump. Screening mammography is conducted for women with no symptoms of breast cancer, for early detection of cancer when the cancer is most treatable an...

This paper presents an approach for breast cancer diagnosis in digital mammogram using curvelet transform. After decomposing the mammogram images in curvelet basis, a special set of the biggest coefficients is extracted as feature vector. The Euclidean distance is then used to construct a supervised classifier. The experimental results gave a 98.59% classification accuracy rate, which indicate ...

Breast cancer is one of the leading causes of death among the women. Mammogram analysis is the most effective method that helps in the early detection of breast cancer. Microcalcification, masses, and architectural detection in the mammogram plays an important role in the later stages of diagnosis. In this paper we propose an effective method for the detection and classification of clustered mi...

Early detection of breast cancer increases the survival rate and increases the treatment options. In this paper, we present different preprocessing methods for mammogram images. The main goal of the preprocessing is to improve the mammogram image quality and make it ready for the segmentation and feature extraction. Different types of filtering techniques are available for preprocessing. Filter...

Mammogram images classification using data mining methods review on past literature showed that these methods are relatively successful however accuracy and efficiency are still outstanding issues. Therefore, the positive reviews produced from past works on fuzzy soft set based classification have resulted in an attempt to use similarity approach on fuzzy soft set for mammogram images classific...

Mammogram classification is directly related to computer-aided diagnosis of breast cancer. Traditional methods requires great effort to annotate the training data by costly manual labeling and specialized computational models to detect these annotations during test. Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning for labeling a...

This paper examines the results of two initial studies of the problemsolving strategies used by more and less skilled medical professionals during mammogram interpretation. The first study examined the cognitive processing of staff radiologists and radiology residents, while the second looked at surgical residents and medical students‚ as they individually solved a set of breast disease cases. ...