Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection
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Date
2017-02-10
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Abstract
Breast cancer is the most prevalent cancer that affects women all over the world. Early detection
and treatment of breast cancer could decline the mortality rate. Some issues such as technical
reasons, which related to imaging quality and human error, increase misdiagnosis of breast cancer
by radiologists. Computer-aided detection systems (CADs) are developed to overcome these
restrictions and have been studied in many imaging modalities for breast cancer detection in recent
years. The CAD systems improve radiologists’ performance in finding and discriminat- ing between
the normal and abnormal tissues. These procedures are performed only as a double reader but the
absolute decisions are still made by the radiologist. In this study, the recent CAD systems for
breast cancer detec- tion on different modalities such as mammography, ultrasound, MRI, and biopsy
histopathological images are introduced. The foundation of CAD systems generally consist of four
stages: Pre-processing, Segmentation, Fea- ture extraction, and Classification. The approaches
which applied to design different stages of CAD system are summarised. Advantages and disadvantages
of different segmentation, feature extraction and classification tech- niques are listed.
In addition, the impact of imbalanced datasets in classification outcomes and appropriate methods to
solve these issues are discussed. As well as, performance evaluation metrics for various stages of
breast cancer detection CAD systems are reviewed.
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Keywords
breast cancer, computer-aided diagnosis system, segmentation, feature extraction, classification