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A Novel neural-genetic algorithm to find the most significant combination of features in digital mammograms

หน่วยงาน Central Queensland University, Australia

รายละเอียด

ชื่อเรื่อง : A Novel neural-genetic algorithm to find the most significant combination of features in digital mammograms
นักวิจัย : Verma, Brijesh. , Zhang, Ping.
คำค้น : Genetic algorithms. , 700102 Application tools and system utilities. , 280207 Pattern Recognition , 280212 Neural Networks, Genetic Alogrithms and Fuzzy Logic , 280208 Computer Vision , Breast , Neural networks (Computer science) , 890202 Application Tools and System Utilities. , 8902 Computer Software and Services. , 89 Information and Communication Services. , 080109 Pattern Recognition and Data Mining. , 0801 Artificial Intelligence and Image Processing. , 08 Information and Computing Sciences. , 080108 Neural, Evolutionary and Fuzzy Computation. , 080104 Computer Vision. , Feature selection -- Neural networks -- Genetic algorithms -- Digital mammography
หน่วยงาน : Central Queensland University, Australia
ผู้ร่วมงาน : -
ปีพิมพ์ : 2550
อ้างอิง : http://hdl.cqu.edu.au/10018/12465 , http://dx.doi.org/10.1016/j.asoc.2005.02.008
ที่มา : Verma, B & Zhang, P 2007, 'A novel neural-genetic algorithm to find the most significant combination of features in digital mammograms', Applied Soft Computing, v. 7 no. 2, pp. 612-625. http://dx.doi.org/10.1016/j.asoc.2005.02.008
ความเชี่ยวชาญ : -
ความสัมพันธ์ : Applied soft computing. Netherlands : Elsevier, 2007. Vol 7, issue 2 (March 2007), p. 612-625 14 pages Refereed 1568-4946 , 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 a neural-genetic algorithm for feature selection to classify microcalcification patterns in digital mammograms. It aims to develop a step-wise algorithm to find the best feature set and a suitable neural architecture for microcalcification classification. The obtained results show that the proposed algorithm is able to find an appropriate feature subset, which also produces a high classification rate.

บรรณานุกรม :
Verma, Brijesh. , Zhang, Ping. . (2550). A Novel neural-genetic algorithm to find the most significant combination of features in digital mammograms.
    กรุงเทพมหานคร : Central Queensland University, Australia.
Verma, Brijesh. , Zhang, Ping. . 2550. "A Novel neural-genetic algorithm to find the most significant combination of features in digital mammograms".
    กรุงเทพมหานคร : Central Queensland University, Australia.
Verma, Brijesh. , Zhang, Ping. . "A Novel neural-genetic algorithm to find the most significant combination of features in digital mammograms."
    กรุงเทพมหานคร : Central Queensland University, Australia, 2550. Print.
Verma, Brijesh. , Zhang, Ping. . A Novel neural-genetic algorithm to find the most significant combination of features in digital mammograms. กรุงเทพมหานคร : Central Queensland University, Australia; 2550.