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Breast Cancer Diagnosis With Mammography: Recent Advances on CBMR-Based CAD Systems
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Author(s): Abir Baâzaoui (SIIVA, LIMTIC Laboratory, Institut Supérieur d'Informatique El Manar, Université de Tunis El Manar, Tunisia)and Walid Barhoumi (Ecole Nationale d'Ingénieurs de Carthage, Université de Carthage, Tunisia & SIIVA, LIMTIC Laboratory, Institut Supérieur d'Informatique El Manar, Université de Tunis El Manar, Tunisia)
Copyright: 2021
Pages: 21
Source title:
Biomedical Computing for Breast Cancer Detection and Diagnosis
Source Author(s)/Editor(s): Wellington Pinheiro dos Santos (Universidade Federal de Pernambuco, Brazil), Washington Wagner Azevedo da Silva (Universidade Federal de Pernambuco, Brazil)and Maira Araujo de Santana (Universidade Federal de Pernambuco, Brazil)
DOI: 10.4018/978-1-7998-3456-4.ch006
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Abstract
Breast cancer, which is the second-most common and leading cause of cancer death among women, has witnessed growing interest in the two last decades. Fortunately, its early detection is the most effective way to detect and diagnose breast cancer. Although mammography is the gold standard for screening, its difficult interpretation leads to an increase in missed cancers and misinterpreted non-cancerous lesion rates. Therefore, computer-aided diagnosis (CAD) systems can be a great helpful tool for assisting radiologists in mammogram interpretation. Nonetheless, these systems are limited by their black-box outputs, which decreases the radiologists' confidence. To circumvent this limit, content-based mammogram retrieval (CBMR) is used as an alternative to traditional CAD systems. Herein, authors systematically review the state-of-the-art on mammography-based breast cancer CAD methods, while focusing on recent advances in CBMR methods. In order to have a complete review, mammography imaging principles and its correlation with breast anatomy are also discussed.
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