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   Vol.E76-D   No.8   pp.956-962
Publication Date: 1993/08/25
Online ISSN: 
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>>
Buy this Article

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.