ridm@nrct.go.th   ระบบคลังข้อมูลงานวิจัยไทย   รายการโปรดที่คุณเลือกไว้

Characterization of breast abnormality patterns in digital mammograms using auto-associator neural network

หน่วยงาน Central Queensland University, Australia

รายละเอียด

ชื่อเรื่อง : Characterization of breast abnormality patterns in digital mammograms using auto-associator neural network
นักวิจัย : Panchal, Rinku. , Verma, Brijesh.
คำค้น : Breast , TBA , 700102 Application tools and system utilities , 280212 Neural Networks, Genetic Alogrithms and Fuzzy Logic , 280207 Pattern Recognition , 280208 Computer Vision , Auto-associator network -- Classifiers -- Digital mammography
หน่วยงาน : Central Queensland University, Australia
ผู้ร่วมงาน : -
ปีพิมพ์ : 2549
อ้างอิง : http://hdl.cqu.edu.au/10018/7367 , 0302-9743 , , cqu:299 ,
ที่มา : Panchal, R & Verma, B 2006 'Characterization of Breast Abnormality Patterns in Digital Mammograms Using Auto-associator Neural Network'. Proceedings of the 13th International Conference on Neural Information Processing (ICONIP2006), Hong Kong, China, 3rd-6th October, 2006, pp. 127-136.
ความเชี่ยวชาญ : -
ความสัมพันธ์ : Proceedings of the 13th International Conference on Neural Information Processing (ICONIP2006) Berlin, Germany : Springer-Verlag, 2006. p.127-136 1164 pages Refereed 0302-9743 , aCQUIRe [electronic resource] : Central Queensland University Institutional Repository.
ขอบเขตของเนื้อหา : -
บทคัดย่อ/คำอธิบาย :

Presence of mass in breast tissues is highly indicative of breast cancer. The research work investigates the significance of neural-association of mass type of breast abnormality patterns for benign and malignant class characterization using auto-associator neural network and original features. The characterized patterns are finally classified into benign and malignant classes using a classifier neural network. Grey-level based statistical features, BIRADS features, patient age feature and subtlety value feature have been used in proposed research work. The proposed research technique attained a 94% testing classification rate with a 100% training classification rate on digital mammograms taken from the DDSM benchmark database.

บรรณานุกรม :
Panchal, Rinku. , Verma, Brijesh. . (2549). Characterization of breast abnormality patterns in digital mammograms using auto-associator neural network.
    กรุงเทพมหานคร : Central Queensland University, Australia.
Panchal, Rinku. , Verma, Brijesh. . 2549. "Characterization of breast abnormality patterns in digital mammograms using auto-associator neural network".
    กรุงเทพมหานคร : Central Queensland University, Australia.
Panchal, Rinku. , Verma, Brijesh. . "Characterization of breast abnormality patterns in digital mammograms using auto-associator neural network."
    กรุงเทพมหานคร : Central Queensland University, Australia, 2549. Print.
Panchal, Rinku. , Verma, Brijesh. . Characterization of breast abnormality patterns in digital mammograms using auto-associator neural network. กรุงเทพมหานคร : Central Queensland University, Australia; 2549.