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Novel network architecture and learning algorithm for the classification of mass abnormalities in digitized mammograms

หน่วยงาน Central Queensland University, Australia

รายละเอียด

ชื่อเรื่อง : Novel network architecture and learning algorithm for the classification of mass abnormalities in digitized mammograms
นักวิจัย : Verma, Brijesh.
คำค้น : Applied research. , 890202 Application Tools and System Utilities. , 920203 Diagnostic Methods. , 080108 Neural, Evolutionary and Fuzzy Computation. , 080109 Pattern Recognition and Data Mining. , Breast -- Radiography. , Neural networks (Computer science) , Pattern recognition systems. , Genetic algorithms. , Learning Algorithms -- Neural Networks -- Digital mammography
หน่วยงาน : Central Queensland University, Australia
ผู้ร่วมงาน : -
ปีพิมพ์ : 2551
อ้างอิง : http://hdl.cqu.edu.au/10018/28309 , http://dx.doi.org/10.1016/j.artmed.2007.09.003. , cqu:4332
ที่มา : Verma, B 2008, 'Novel network architecture and learning algorithm for the classification of mass abnormalities in digitized mammograms', Artificial Intelligence in Medicine, vol. 42 no. 1, pp. 1-94.
ความเชี่ยวชาญ : -
ความสัมพันธ์ : Artificial intelligence in medicine. Netherlands. : Elsevier, 2008. Vol. 42, no. 1, (January 2008), p. 67-79 13 pages Refereed 0933-3657 , ACQUIRE [electronic resource] : Central Queensland University Institutional Repository.
ขอบเขตของเนื้อหา : -
บทคัดย่อ/คำอธิบาย :

The main objective of this paper is to present a novel learning algorithm for the classification of mass abnormalities in digitized mammograms. The proposed approach consists of new network architecture and a new learning algorithm. The original idea is based on the introduction of an additional neuron in the hidden layer for each output class. The additional neurons for benign and malignant classes help in improving memorization ability without destroying the generalization ability of the network. The training is conducted by combining minimal distance based similarity/random weights and direct calculation of output weights. The proposed approach can memorize training patterns with 100% retrieval accuracy as well as achieve high generalization accuracy for patterns which it has never seen before. The grey-level and breast imaging reporting and data system based features from digitized mammograms are extracted and used to train the network with the proposed architecture and learning algorithm. The best results achieved by using the proposed approach are 100% on training set and 94% on test set.

บรรณานุกรม :
Verma, Brijesh. . (2551). Novel network architecture and learning algorithm for the classification of mass abnormalities in digitized mammograms.
    กรุงเทพมหานคร : Central Queensland University, Australia.
Verma, Brijesh. . 2551. "Novel network architecture and learning algorithm for the classification of mass abnormalities in digitized mammograms".
    กรุงเทพมหานคร : Central Queensland University, Australia.
Verma, Brijesh. . "Novel network architecture and learning algorithm for the classification of mass abnormalities in digitized mammograms."
    กรุงเทพมหานคร : Central Queensland University, Australia, 2551. Print.
Verma, Brijesh. . Novel network architecture and learning algorithm for the classification of mass abnormalities in digitized mammograms. กรุงเทพมหานคร : Central Queensland University, Australia; 2551.