A review of the current state of the computer-aided diagnosis (CAD) systems for breast cancer diagnosis

被引:23
作者
Guo, Zicheng [1 ]
Xie, Jiping [1 ]
Wan, Yi [1 ]
Zhang, Min [1 ]
Qiao, Liang [1 ]
Yu, Jiaxuan [1 ]
Chen, Sijing [1 ]
Li, Bingxin [1 ]
Yao, Yongqiang [1 ]
机构
[1] Dalian Univ, Dept Breast & Thyroid Surg, Affiliated Zhongshan Hosp, 6 Jiefang Rd, Dalian 116001, Peoples R China
关键词
breast cancer; mammography; computer-aided diagnosis; ultrasound; MAMMOGRAPHIC DENSITY; LESIONS; RISK; FEATURES; MASSES;
D O I
10.1515/biol-2022-0517
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Breast cancer is one of the most common cancers affecting females worldwide. Early detection and diagnosis of breast cancer may aid in timely treatment, reducing the mortality rate to a great extent. To diagnose breast cancer, computer-aided diagnosis (CAD) systems employ a variety of imaging modalities such as mammography, computerized tomography, magnetic resonance imaging, ultrasound, and histological imaging. CAD and breast-imaging specialists are in high demand for early detection and diagnosis. This system has the potential to enhance the partiality of traditional histopathological image analysis. This review aims to highlight the recent advancements and the current state of CAD systems for breast cancer detection using different modalities.
引用
收藏
页码:1600 / 1611
页数:12
相关论文
共 58 条
[1]   Hybrid Mammogram Classification Using Rough Set and Fuzzy Classifier [J].
Abu-Amara, Fadi ;
Abdel-Qader, Ikhlas .
INTERNATIONAL JOURNAL OF BIOMEDICAL IMAGING, 2009, 2009
[2]   Liquid biopsy in breast cancer: A comprehensive review [J].
Alimirzaie, Sahar ;
Bagherzadeh, Maryam ;
Akbari, Mohammad R. .
CLINICAL GENETICS, 2019, 95 (06) :643-660
[3]   A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets [J].
Antropova, Natalia ;
Huynh, Benjamin Q. ;
Giger, Maryellen L. .
MEDICAL PHYSICS, 2017, 44 (10) :5162-5171
[4]   Management of High-Risk Breast Lesions [J].
Bahl, Manisha .
RADIOLOGIC CLINICS OF NORTH AMERICA, 2021, 59 (01) :29-40
[5]   Mammographic density and the risk and detection of breast cancer [J].
Boyd, Norman F. ;
Guo, Helen ;
Martin, Lisa J. ;
Sun, Limei ;
Stone, Jennifer ;
Fishell, Eve ;
Jong, Roberta A. ;
Hislop, Greg ;
Chiarelli, Anna ;
Minkin, Salomon ;
Yaffe, Martin J. .
NEW ENGLAND JOURNAL OF MEDICINE, 2007, 356 (03) :227-236
[6]  
Bratinčević L, 2022, Radiološki vjesnik, V46, P2, DOI [10.55378/rv.46.1.1, 10.55378/rv.46.1.1]
[7]  
Castellino Ronald A, 2005, Cancer Imaging, V5, P17, DOI 10.1102/1470-7330.2005.0018
[8]   Evaluation of the Accuracy of a Computer-aided Diagnosis (CAD) System in Breast Ultrasound according to the Radiologist's Experience [J].
Chabi, Marie-Laure ;
Borget, Isabelle ;
Ardiles, Rosario ;
Aboud, Ghassen ;
Boussouar, Samia ;
Vilar, Vanessa ;
Dromain, Clarisse ;
Balleyguier, Corinne .
ACADEMIC RADIOLOGY, 2012, 19 (03) :311-319
[9]   Breast lesions on sonograms: Computer-aided diagnosis with nearly setting-independent features and artificial neural networks [J].
Chen, CM ;
Chou, YH ;
Han, KC ;
Hung, GS ;
Tiu, CM ;
Chiou, HJ ;
Chiou, SY .
RADIOLOGY, 2003, 226 (02) :504-514
[10]   Characterization of solid breast masses -: Use of the sonographic breast imaging reporting and data system lexicon [J].
Costantini, Melania ;
Belli, Paolo ;
Lombardi, Roberta ;
Franceschini, Gianluca ;
Mule, Antonino ;
Bonomo, Lorenzo .
JOURNAL OF ULTRASOUND IN MEDICINE, 2006, 25 (05) :649-659