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Breast cancer is reported to be the second deadliest cancer among cancerous woman. Statistics show that the case of breast cancer in the world is increasing every year. By analyzing a mammogram, pathologists could detect the presence of micro calcification in ones breast. However, micro calcification could be classified into benign and malignant. The later indicates the presence of cancer. Computer-Aided...
In recent years, many Computer Aided Diagnosis (CAD) systems are suggested. Those systems can diagnose instead of a doctor, thus they are expected to reduce heavy burdens on the doctor during screening. The purpose of this study is to improve detection sensitivity for masses reducing the number of false positives as well as to extract mass regions accurately. In the proposed method, we focused on...
X-ray mammograms are one of the most common techniques used by radiologists for breast cancer detection and diagnosis. Early detection is important, which raised the importance of developing Computer-Aided Detection and Diag-nosis(CAD) systems. Although most(CAD)systems were designed to help radiologists in their diagnosis by providing useful insight, the accuracy of CAD systems remains below the...
In this work we propose an image-retrieval based approach for case-adaptive classifier design in computer-aided diagnosis (CAD). The traditional approach in CAD is to first train a pattern-classifier based on a set of existing training samples, and then apply this classifier to subsequent new cases. In our proposed approach, we will first apply image-retrieval to obtain a set of lesion images from...
Breast cancer is a life threatening disease affecting one of every eight women, with the risk increasing significantly with age. Non-invasive diagnostic modalities are preferable over biopsy. However, these non-invasive diagnostic techniques have lower diagnostic accuracy when compared to biopsy. Currently, CAD (Computer-Aided Diagnosis) techniques have demonstrated strong potential to increase the...
A semantic-based CAD system for mammography is proposed. Semantic layer is added to the computer aided diagnosis procedure by comprehend the lesion symptom of mammogram and clinical information. This provides doctor a complete and standardization semantic interpretation of the case. Final result is obtained through the synthetic analysis of the acquired semantic information by a Bayesian semantic...
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