For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Breast Tumor Classification by Neural Networks Fed with Sequential-Dependence Factors to the Input Layer
Du-Yih TSAI Hiroshi FUJITA Katsuhei HORITA Tokiko ENDO Choichiro KIDO Sadayuki SAKUMA
IEICE TRANSACTIONS on Information and Systems
Publication Date: 1993/08/25
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Medical Electronics and Medical Information
image processing, feature extraction, neural network, sequential dependence, mammography,
Full Text: PDF(671.2KB)>>
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.