Neural vs. statistical classifier in conjunction with genetic algorithm feature selection in digital mammography

Ping Zhang; Brijesh Varma; Kuldeep Kumar

doi:10.1109/CEC.2003.1299806

Neural vs. statistical classifier in conjunction with genetic algorithm feature selection in digital mammography

Ping Zhang, Brijesh Varma, Kuldeep Kumar

Bond Business School

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

12 Citations (Scopus)

Abstract

Digital mammography is one of the most suitable methods for early detection of breast cancer. It uses digital mammograms to find suspicious areas containing benign and malignant microcalcifications. However, it is very difficult to distinguish benign and malignant microcalcifications. This is reflected in the high percentage of unnecessary biopsies that are performed and many deaths caused by late detection or misdiagnosis. A computer based feature selection and classification system can provide a second opinion to the radiologists in assessment of microcalcifications. The research proposes and investigates a neural-genetic algorithm for feature selection in conjunction with neural and statistical classifiers to classify microcalcification patterns in digital mammograms. The obtained results show that the proposed approach is able to find an appropriate feature subset and neural classifier achieves better results than two statistical models.

Original language	English
Title of host publication	2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings
Publisher	IEEE Computer Society
Pages	1206-1213
Number of pages	8
Volume	2
DOIs	https://doi.org/10.1109/CEC.2003.1299806
Publication status	Published - 2003
Event	2003 Congress on Evolutionary Computation, CEC 2003 - Canberra, ACT, Australia Duration: 8 Dec 2003 → 12 Dec 2003

Conference

Conference	2003 Congress on Evolutionary Computation, CEC 2003
Country/Territory	Australia
City	Canberra, ACT
Period	8/12/03 → 12/12/03

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/CEC.2003.1299806

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title = "Neural vs. statistical classifier in conjunction with genetic algorithm feature selection in digital mammography",

abstract = "Digital mammography is one of the most suitable methods for early detection of breast cancer. It uses digital mammograms to find suspicious areas containing benign and malignant microcalcifications. However, it is very difficult to distinguish benign and malignant microcalcifications. This is reflected in the high percentage of unnecessary biopsies that are performed and many deaths caused by late detection or misdiagnosis. A computer based feature selection and classification system can provide a second opinion to the radiologists in assessment of microcalcifications. The research proposes and investigates a neural-genetic algorithm for feature selection in conjunction with neural and statistical classifiers to classify microcalcification patterns in digital mammograms. The obtained results show that the proposed approach is able to find an appropriate feature subset and neural classifier achieves better results than two statistical models.",

author = "Ping Zhang and Brijesh Varma and Kuldeep Kumar",

year = "2003",

doi = "10.1109/CEC.2003.1299806",

language = "English",

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pages = "1206--1213",

booktitle = "2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings",

publisher = "IEEE Computer Society",

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note = "2003 Congress on Evolutionary Computation, CEC 2003 ; Conference date: 08-12-2003 Through 12-12-2003",

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Zhang, P, Varma, B & Kumar, K 2003, Neural vs. statistical classifier in conjunction with genetic algorithm feature selection in digital mammography. in 2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings. vol. 2, 1299806, IEEE Computer Society, pp. 1206-1213, 2003 Congress on Evolutionary Computation, CEC 2003, Canberra, ACT, Australia, 8/12/03. https://doi.org/10.1109/CEC.2003.1299806

Neural vs. statistical classifier in conjunction with genetic algorithm feature selection in digital mammography. / Zhang, Ping; Varma, Brijesh; Kumar, Kuldeep.
2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings. Vol. 2 IEEE Computer Society, 2003. p. 1206-1213 1299806.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

TY - GEN

T1 - Neural vs. statistical classifier in conjunction with genetic algorithm feature selection in digital mammography

AU - Zhang, Ping

AU - Varma, Brijesh

AU - Kumar, Kuldeep

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N2 - Digital mammography is one of the most suitable methods for early detection of breast cancer. It uses digital mammograms to find suspicious areas containing benign and malignant microcalcifications. However, it is very difficult to distinguish benign and malignant microcalcifications. This is reflected in the high percentage of unnecessary biopsies that are performed and many deaths caused by late detection or misdiagnosis. A computer based feature selection and classification system can provide a second opinion to the radiologists in assessment of microcalcifications. The research proposes and investigates a neural-genetic algorithm for feature selection in conjunction with neural and statistical classifiers to classify microcalcification patterns in digital mammograms. The obtained results show that the proposed approach is able to find an appropriate feature subset and neural classifier achieves better results than two statistical models.

AB - Digital mammography is one of the most suitable methods for early detection of breast cancer. It uses digital mammograms to find suspicious areas containing benign and malignant microcalcifications. However, it is very difficult to distinguish benign and malignant microcalcifications. This is reflected in the high percentage of unnecessary biopsies that are performed and many deaths caused by late detection or misdiagnosis. A computer based feature selection and classification system can provide a second opinion to the radiologists in assessment of microcalcifications. The research proposes and investigates a neural-genetic algorithm for feature selection in conjunction with neural and statistical classifiers to classify microcalcification patterns in digital mammograms. The obtained results show that the proposed approach is able to find an appropriate feature subset and neural classifier achieves better results than two statistical models.

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BT - 2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings

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T2 - 2003 Congress on Evolutionary Computation, CEC 2003

Y2 - 8 December 2003 through 12 December 2003

ER -

Neural vs. statistical classifier in conjunction with genetic algorithm feature selection in digital mammography

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