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Neural vs statistical classifier in conjunction with genetic algorithm based feature selection

หน่วยงาน Central Queensland University, Australia

รายละเอียด

ชื่อเรื่อง : Neural vs statistical classifier in conjunction with genetic algorithm based feature selection
นักวิจัย : Zhang, Ping. , Verma, Brijesh. , Kumar, Kuldeep.
คำค้น : 700102 Application tools and system utilities. , 280212 Neural Networks, Genetic Alogrithms and Fuzzy Logic , Pattern recognition systems. , Breast , Genetic algorithms. , Neural networks (Computer science) , 890202 Application Tools and System Utilities. , 8902 Computer Software and Services. , 89 Information and Communication Services. , 080108 Neural, Evolutionary and Fuzzy Computation. , 0801 Artificial Intelligence and Image Processing. , 08 Information and Computing Sciences. , Microcalcifications pattern classification -- Neural networks -- Statistical methods -- Feature selection -- Genetic algorithm
หน่วยงาน : Central Queensland University, Australia
ผู้ร่วมงาน : -
ปีพิมพ์ : 2548
อ้างอิง : http://hdl.cqu.edu.au/10018/2838 , http:// dx.doi.org/10.1016/j.patrec.2004.09.053 , cqu:1698
ที่มา : Zhang, P, Verma, B & Kumar, K 2005, 'Neural vs statistical classifier in conjunction with genetic algorithm based feature selection', Pattern Recognition Letters, vol. 26, no. 7, pp. 909-919. http:// dx.doi.org/10.1016/j.patrec.2004.09.053
ความเชี่ยวชาญ : -
ความสัมพันธ์ : Pattern recognition letters. Netherlands : Elsevier, 2005. Vol. 26, no. 7 (May 2005), p. 909-919 11 pages Refereed 0167-8655 (online) , ACQUIRE [electronic resource] : Central Queensland University Institutional Repository.
ขอบเขตของเนื้อหา : -
บทคัดย่อ/คำอธิบาย :

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 classifymicrocalcification patterns in digitalmammograms. 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 modes.

บรรณานุกรม :
Zhang, Ping. , Verma, Brijesh. , Kumar, Kuldeep. . (2548). Neural vs statistical classifier in conjunction with genetic algorithm based feature selection.
    กรุงเทพมหานคร : Central Queensland University, Australia.
Zhang, Ping. , Verma, Brijesh. , Kumar, Kuldeep. . 2548. "Neural vs statistical classifier in conjunction with genetic algorithm based feature selection".
    กรุงเทพมหานคร : Central Queensland University, Australia.
Zhang, Ping. , Verma, Brijesh. , Kumar, Kuldeep. . "Neural vs statistical classifier in conjunction with genetic algorithm based feature selection."
    กรุงเทพมหานคร : Central Queensland University, Australia, 2548. Print.
Zhang, Ping. , Verma, Brijesh. , Kumar, Kuldeep. . Neural vs statistical classifier in conjunction with genetic algorithm based feature selection. กรุงเทพมหานคร : Central Queensland University, Australia; 2548.