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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: 0.5] [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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HUANG SHARINA, ZHAO GUOLIANG. A COMPARISON BETWEEN QUANTUM INSPIRED BACTERIAL FORAGING ALGORITHM AND GA-LIKE ALGORITHM FOR GLOBAL OPTIMIZATION. INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2012. [DOI: 10.1142/s1469026812500162] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Bacterial foraging algorithm (BFA) is a population-based stochastic search technique for solving various scientific and engineering problems. However, it is inefficient in some practical situations. In order to improve the performance of the BFA, we propose a novel optimization algorithm, named quantum inspired bacterial foraging algorithm (QBFA), which applies several quantum computing principles, and a new mechanism is proposed to encode and observe the population. The algorithm has been evaluated on the standard high-dimensional benchmark functions in comparison with GA, PSO, GSO and FBSA, respectively. The proposed algorithm is then used to tune a PID controller of an automatic voltage regulator (AVR) system. Simulation results clearly illustrate that the proposed approach is very efficient and could be easily extended to 300 or higher-dimensional problems.
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Affiliation(s)
- SHARINA HUANG
- School of Science, Heilongjiang Institute of Science and Technology, Harbin 150027, People's Republic of China
| | - GUOLIANG ZHAO
- School of Science, Heilongjiang Institute of Science and Technology, Harbin 150027, People's Republic of China
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