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A neural-genetic algorithm for feature selection and breast abnormality classification in digital mammography

หน่วยงาน Central Queensland University, Australia

รายละเอียด

ชื่อเรื่อง : A neural-genetic algorithm for feature selection and breast abnormality classification in digital mammography
นักวิจัย : Zhang, Ping. , Verma, Brijesh. , Kumar, K.
คำค้น : 700103 Information processing services. , 280203 Image Processing. , TBA. , 890205 Information Processing Services (incl. Data Entry and Capture) , 8902 Computer Software and Services. , 89 Information and Communication Services. , 080106 Image Processing. , 0801 Artificial Intelligence and Image Processing. , 08 Information and Computing Sciences. , Breast , Breast , Diagnostic imaging , Breast
หน่วยงาน : Central Queensland University, Australia
ผู้ร่วมงาน : -
ปีพิมพ์ : 2547
อ้างอิง : http://hdl.cqu.edu.au/10018/4332 , cqu:2054
ที่มา : Zhang, P, Verma, B & Kumar, K 2004, 'A neural-genetic algorithm for feature selection and breast abnormality classification in digital mammography', paper presented at 2004 Joint International Neural Networks Conference (IJCNN), and of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)., Budapest, Hungary.
ความเชี่ยวชาญ : -
ความสัมพันธ์ : 2004 Joint International Neural Networks Conference (IJCNN), and of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). Piscataway, NJ, USA. : Institute of Electrical and Electronics Engineers, Inc., 2004. p. 2303-2308 6 pages Refereed 0780383605 , 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. However, it is very difficult to distinguish benign and malignant cases, especially for the small size lesions in the early stage of cancer. 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. This paper proposes a neural-genetic algorithm for feature selection in conjunction with neural network based classifier. It also combined the computer-extracted statistical features from the mammogram with the human-extracted features for classifying different types of small size breast abnormalities, It obtained 90.5% accuracy rate for calcification cases and 81.2% for mass cases with different feature subsets. The obtained results show that different types of breast abnormality should use different features for classification.

บรรณานุกรม :
Zhang, Ping. , Verma, Brijesh. , Kumar, K. . (2547). A neural-genetic algorithm for feature selection and breast abnormality classification in digital mammography.
    กรุงเทพมหานคร : Central Queensland University, Australia.
Zhang, Ping. , Verma, Brijesh. , Kumar, K. . 2547. "A neural-genetic algorithm for feature selection and breast abnormality classification in digital mammography".
    กรุงเทพมหานคร : Central Queensland University, Australia.
Zhang, Ping. , Verma, Brijesh. , Kumar, K. . "A neural-genetic algorithm for feature selection and breast abnormality classification in digital mammography."
    กรุงเทพมหานคร : Central Queensland University, Australia, 2547. Print.
Zhang, Ping. , Verma, Brijesh. , Kumar, K. . A neural-genetic algorithm for feature selection and breast abnormality classification in digital mammography. กรุงเทพมหานคร : Central Queensland University, Australia; 2547.