Approaches for automated detection and classification of masses in mammograms

By Heng-Da Cheng, Xiangjun Shi, Rui Min, Liming Hu, Xiaopeng Cai, H. Du. Pattern Recognition, 2006

530 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/34c44883a6152c5298f2c452670c1127072400e6

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In Artificial Intelligence

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances (2009, methodology): “…detection performance, summarized using free-response receiver operating characteristic (FROC) curves, achieved by different representative methods in an evaluation study based on a clinical mammographic database [56]; machine-learning-based meth-ods seemed to have achieved the best performance.”
Kernel PCA for novelty detection (2007, unknown): “A machine learning technique that works well for data with such characteristics would be of great beneﬁt for medical diagnosis, particularly, for the early diagnoses of cancer, which requires the examination of millions of humans [3,4].”
Benign and malignant breast tumors classification based on region growing and CNN segmentation (2015, methodology): “Since MLPs are appropriate tools in pattern recognition (Chauhan et al., 2012; Cheng et al., 2006; Kuo, Hsiao, Huang, & Chen, 2008; Pawar & Patil, 2013; Tahmasbi et al., 2011) they are used in this work.”
A review of automatic mass detection and segmentation in mammographic images (2010, methodology): “Rangayyan et al. (2007) provided a review covering the full range of mammographic abnormalities using an approach similar to Cheng et al. (2006).”
Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach (2014, methodology): “Once a CAD analysis has been done, a radiologist will do a visual check of those areas, and based on training and experience, decide how serious the lumps may actually be [6].”
Automated breast cancer detection and classification using ultrasound images: A survey (2015, background): “…Guoa, Ling Zhangb aDepartment of Computer Science, Utah State University, Logan, UT 84322, USA bSchool of Mathematics and System Science, Shandong University, China A R T I C L E I N F O A B S T R A C T Article history: Received 9 September 2008 Received in revised form 16 April 2009 Accepted 14…”