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A novel min-max feature value based neural architecture and learning algorithm for classification of microcalcifications

หน่วยงาน Central Queensland University, Australia

รายละเอียด

ชื่อเรื่อง : A novel min-max feature value based neural architecture and learning algorithm for classification of microcalcifications
นักวิจัย : Verma, Brijesh. , Panchal, Rinku. , Kumar, Kuldeep.
คำค้น : Breast , TBA. , 890205 Information Processing Services (incl. Data Entry and Capture) , 080611 Information Systems Theory. , Pattern perception.
หน่วยงาน : Central Queensland University, Australia
ผู้ร่วมงาน : -
ปีพิมพ์ : 2546
อ้างอิง : http://hdl.cqu.edu.au/10018/42782 , http://dx.doi.org/10.1109/IJCNN.2003.1223720
ที่มา : Verma, B, Panchal, R & Kumar, K 2003, 'A Novel Min-Max Feature Value Based Neural Architecture And Learning Algorithm For Classification of Microcalcifications' in Proceedings of the International Joint Conference on Neural Networks 2003. Portland, Oregon, July 20 - 24, 2003, pp. 2033-2038. http://dx.doi.org/10.1109/IJCNN.2003.1223720 (viewed 8/4/10)
ความเชี่ยวชาญ : -
ความสัมพันธ์ : Proceedings of the International Joint Conference on Neural Networks 2003, Portland, Oregon, July 20-24 2003. United States. : The Institute of Electrical and Electronics Engineers, 2003. p. 2033 -2038 6 pages Refereed 1098-7576 0780378989 , ACQUIRE [electronic resource] : Central Queensland University Institutional Repository.
ขอบเขตของเนื้อหา : -
บทคัดย่อ/คำอธิบาย :

The paper proposes a novel min-max feature value based neural architecture and learning algorithm for classification of microcalcification patterns in digital mammograms. The neural architecture has a single hidden layer and it has a fixed number of hidden units and outputs. One class is represented by three hidden units and an output. The suspicious areas represented by chain code, are extracted from digital mammograms. The feature values are extracted for benign and malignant microcalcifications. A set of min, average and max values for every input feature is defined and assigned to the weights between input and hidden layer. The weights of the output layer are calculated using least squares methods or assigned in such a way that it maximizes the output value for only one class. Many experiments were conducted on a benchmark database of digital mammograms and comparative results are included in this paper.

บรรณานุกรม :
Verma, Brijesh. , Panchal, Rinku. , Kumar, Kuldeep. . (2546). A novel min-max feature value based neural architecture and learning algorithm for classification of microcalcifications.
    กรุงเทพมหานคร : Central Queensland University, Australia.
Verma, Brijesh. , Panchal, Rinku. , Kumar, Kuldeep. . 2546. "A novel min-max feature value based neural architecture and learning algorithm for classification of microcalcifications".
    กรุงเทพมหานคร : Central Queensland University, Australia.
Verma, Brijesh. , Panchal, Rinku. , Kumar, Kuldeep. . "A novel min-max feature value based neural architecture and learning algorithm for classification of microcalcifications."
    กรุงเทพมหานคร : Central Queensland University, Australia, 2546. Print.
Verma, Brijesh. , Panchal, Rinku. , Kumar, Kuldeep. . A novel min-max feature value based neural architecture and learning algorithm for classification of microcalcifications. กรุงเทพมหานคร : Central Queensland University, Australia; 2546.