Neural vs. statistical classifier in conjunction with genetic algorithm based feature selection
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 reflec...
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| Published in | Pattern recognition letters Vol. 26; no. 7; pp. 909 - 919 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
15.05.2005
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0167-8655 1872-7344 |
| DOI | 10.1016/j.patrec.2004.09.053 |
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| 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 in this paper 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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| AbstractList | 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 in this paper 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 | Kumar, Kuldeep Verma, Brijesh Zhang, Ping |
| Author_xml | – sequence: 1 givenname: Ping surname: Zhang fullname: Zhang, Ping email: pzhang@staff.bond.edu.au organization: School of Information Technology, Bond University, Gold Coast 4229, Australia – sequence: 2 givenname: Brijesh surname: Verma fullname: Verma, Brijesh email: b.verma@cqu.edu.au organization: School of Information Technology, Central Queensland University, Rockhampton 4702, Australia – sequence: 3 givenname: Kuldeep surname: Kumar fullname: Kumar, Kuldeep email: kkumar@staff.bond.edu.au organization: School of Information Technology, Bond University, Gold Coast 4229, Australia |
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| Cites_doi | 10.1142/S0218001493000686 10.1142/S0218001493000674 10.1142/S0218001493000698 10.1109/4233.908389 10.1016/S1076-6332(03)80187-3 10.1109/34.709601 10.1007/BF03325093 10.1148/radiology.198.3.8628853 10.1109/42.293919 10.1016/S1076-6332(03)80718-3 |
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| Keywords | Microcalcifications pattern classification Feature selection Statistical methods Neural networks Genetic algorithm |
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| References_xml | – reference: Guerra-Salcedo, C., Whitley, D., 1999. Genetic Approach to Feature Selection for Ensemble Creation, GECCO-99, – volume: 20 start-page: 832 year: 1998 end-page: 844 ident: bib8 article-title: The random subspace method for constructing decision forests publication-title: IEEE Trans. Pattern Anal. Machine Intell. – reference: Breast Cancer Facts, 2002. – volume: 9 start-page: 420 year: 2002 end-page: 429 ident: bib17 article-title: Optimal neural network architecture selection: improvement in computerized detection of microcalcifications publication-title: Acad Radiol. – start-page: 57 year: 1996 end-page: 58 ident: bib4 article-title: A weighted nearest neighbor algorithm for learning with symbolic features publication-title: Machine Learning – year: 1992 ident: bib16 article-title: Discriminant Analysis and Statistical Pattern Recognition – reference: Verma, B. K., 1998. 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Medical Imaging doi: 10.1109/42.293919 – ident: 10.1016/j.patrec.2004.09.053_bib20 – volume: 3 start-page: 711 year: 1994 ident: 10.1016/j.patrec.2004.09.053_bib18 article-title: Adaptive multistage nonlinear filtering and wavelet for medical image enhancement publication-title: ICIP – volume: 8 start-page: 1074 year: 2001 ident: 10.1016/j.patrec.2004.09.053_bib19 article-title: Digital mammography: wavelet transform and kalman-filtering neural network in mass segmentation and detection publication-title: Acad Radiol. doi: 10.1016/S1076-6332(03)80718-3 – start-page: 3437 year: 1994 ident: 10.1016/j.patrec.2004.09.053_bib26 article-title: Multistage neural network for pattern recognition in mammogram screening publication-title: IEEE Internat. Conf. Neural Networks ICNN |
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| SubjectTerms | Feature selection Genetic algorithm Microcalcifications pattern classification Neural networks Statistical methods |
| Title | Neural vs. statistical classifier in conjunction with genetic algorithm based feature selection |
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