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Gnanasekaran VS, Joypaul S, Sundaram PM. A Survey on Machine Learning Algorithms for the Diagnosis of Breast Masses with Mammograms. Curr Med Imaging 2020; 16:639-652. [DOI: 10.2174/1573405615666190903141554] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2019] [Revised: 07/08/2019] [Accepted: 07/17/2019] [Indexed: 01/22/2023]
Abstract
Breast cancer is leading cancer among women for the past 60 years. There are no effective
mechanisms for completely preventing breast cancer. Rather it can be detected at its earlier
stages so that unnecessary biopsy can be reduced. Although there are several imaging modalities
available for capturing the abnormalities in breasts, mammography is the most commonly used
technique, because of its low cost. Computer-Aided Detection (CAD) system plays a key role in
analyzing the mammogram images to diagnose the abnormalities. CAD assists the radiologists for
diagnosis. This paper intends to provide an outline of the state-of-the-art machine learning algorithms
used in the detection of breast cancer developed in recent years. We begin the review with
a concise introduction about the fundamental concepts related to mammograms and CAD systems.
We then focus on the techniques used in the diagnosis of breast cancer with mammograms.
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Affiliation(s)
| | - Sutha Joypaul
- AAA College of Engineering and Technology, Sivakasi 626123, Virudhunagar District, Tamil Nadu, India
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Evolving multi-dimensional wavelet neural networks for classification using Cartesian Genetic Programming. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2017.03.048] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Ren Y, Zhang L, Suganthan P. Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article]. IEEE COMPUT INTELL M 2016. [DOI: 10.1109/mci.2015.2471235] [Citation(s) in RCA: 356] [Impact Index Per Article: 39.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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