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Breast cancer is the most common cancer in many countries all over the world. Early detection of cancer, in either diagnosis or screening programs, decreases the mortality rates. Computer Aided Detection (CAD) is software that aids radiologists in detecting abnormalities in medical images. In this article we present our approach in detecting abnormalities in mammograms using digital mammography. Each...
The classification of breast masses into benign and malignant categories plays an important role in the area of computer-aided diagnosis (CAD) of breast cancer. In this paper, one novel scheme based on multi-view information fusion is proposed, in order to improve the accuracy and the robustness of the classification and reduce the false positive rates. Five contour and shape features of the masses...
This work aims at selecting useful features in critical angles and distances by Gray Level Co-occurrence Matrix (GLCM). In this project, images were labeled based on physician opinion in two groups (malignant or benign). These labeled images were used in classification analysis. Images were opened and read in Matlab software. The tumors were cropped in rectangular shape manually; then graycomatrix...
The classification of breast masses into benign and malignant categories plays an important role in the area of computer-aided diagnosis (CAD) of breast cancer. In this paper, in order to improve the accuracy and the robustness of the classification and reduce the false positive rates, we proposed one novel scheme that was based on information fusion in multi views. A series of contour and shape features...
Although breast sonography is highly accurate at distinguishing solid from cystic lesions, it is less precise when differentiating benign and malignant masses. The goal of this study is to evaluate quantitative methods for differential diagnosis of solid breast masses. Three margin features extracted from B-Mode images, along with age of the patient, were analyzed by logistic regression to classify...
The purpose of this study is to investigate the significance of the multi-agent interactive information fusion algorithm over the matter of identification of breast masses in digitized images. For the lack of enough correlation information between the individual classifiers, the generalization performance of the Bayesian fusion method is sometimes far from the expected level, and thereby the multi-agent...
In this article, we present a new mass description dedicated to differentiate between different mass shapes in mammography. This discrimination aims to reach a better mammography classification rate to be used by radiologists as a second opinion to make the final decision about the malignancy probability of radiographic breast images. Therefore, we used a geometrical feature which is perimeter measurement...
An intelligent computer-aided diagnostics system may be developed to assist the radiologists to recognize the masses/lesions appearing in breast in different groups of benignancy/malignancy. In present work we have attempted to develop a computer assisted treatment planning system implementing Genetic algorithm-based Neuro-fuzzy approaches. The boundary based features of the tumor lesions appearing...
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