IEICE TRANSACTIONS on Information
Breast Tumor Classification by Neural Networks Fed with Sequential-Dependence Factors to the Input Layer
Summary :
We applied an artificial neural network approach identify possible tumors into benign and malignant ones in mammograms. A sequential-dependence technique, which calculates the degree of redundancy or patterning in a sequence, was employed to extract image features from mammographic images. The extracted vectors were then used as input to the network. Our preliminary results show that the neural network can correctly classify benign and malignant tumors at an average rate of 85%. This accuracy rate indicates that the neural network approach with the proposed feature-extraction technique has potential utility in the computer-aided diagnosis of breast cancer.
- Publication
- IEICE TRANSACTIONS on Information Vol.E76-D No.8 pp.956-962
- Publication Date
- 1993/08/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Medical Electronics and Medical Information
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Du-Yih TSAI, Hiroshi FUJITA, Katsuhei HORITA, Tokiko ENDO, Choichiro KIDO, Sadayuki SAKUMA, "Breast Tumor Classification by Neural Networks Fed with Sequential-Dependence Factors to the Input Layer" in IEICE TRANSACTIONS on Information,
vol. E76-D, no. 8, pp. 956-962, August 1993, doi: .
Abstract: We applied an artificial neural network approach identify possible tumors into benign and malignant ones in mammograms. A sequential-dependence technique, which calculates the degree of redundancy or patterning in a sequence, was employed to extract image features from mammographic images. The extracted vectors were then used as input to the network. Our preliminary results show that the neural network can correctly classify benign and malignant tumors at an average rate of 85%. This accuracy rate indicates that the neural network approach with the proposed feature-extraction technique has potential utility in the computer-aided diagnosis of breast cancer.
URL: https://global.ieice.org/en_transactions/information/10.1587/e76-d_8_956/_p
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@ARTICLE{e76-d_8_956,
author={Du-Yih TSAI, Hiroshi FUJITA, Katsuhei HORITA, Tokiko ENDO, Choichiro KIDO, Sadayuki SAKUMA, },
journal={IEICE TRANSACTIONS on Information},
title={Breast Tumor Classification by Neural Networks Fed with Sequential-Dependence Factors to the Input Layer},
year={1993},
volume={E76-D},
number={8},
pages={956-962},
abstract={We applied an artificial neural network approach identify possible tumors into benign and malignant ones in mammograms. A sequential-dependence technique, which calculates the degree of redundancy or patterning in a sequence, was employed to extract image features from mammographic images. The extracted vectors were then used as input to the network. Our preliminary results show that the neural network can correctly classify benign and malignant tumors at an average rate of 85%. This accuracy rate indicates that the neural network approach with the proposed feature-extraction technique has potential utility in the computer-aided diagnosis of breast cancer.},
keywords={},
doi={},
ISSN={},
month={August},}
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TY - JOUR
TI - Breast Tumor Classification by Neural Networks Fed with Sequential-Dependence Factors to the Input Layer
T2 - IEICE TRANSACTIONS on Information
SP - 956
EP - 962
AU - Du-Yih TSAI
AU - Hiroshi FUJITA
AU - Katsuhei HORITA
AU - Tokiko ENDO
AU - Choichiro KIDO
AU - Sadayuki SAKUMA
PY - 1993
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E76-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 1993
AB - We applied an artificial neural network approach identify possible tumors into benign and malignant ones in mammograms. A sequential-dependence technique, which calculates the degree of redundancy or patterning in a sequence, was employed to extract image features from mammographic images. The extracted vectors were then used as input to the network. Our preliminary results show that the neural network can correctly classify benign and malignant tumors at an average rate of 85%. This accuracy rate indicates that the neural network approach with the proposed feature-extraction technique has potential utility in the computer-aided diagnosis of breast cancer.
ER -
English
