Classification of breast abnormalities in digital mammograms using image and BI-RADS features in conjunction with neural network

This paper investigates the significance of combining grey-level based image features and BI-RADS lesion descriptors along with patient age and a subtlety value (radiologists' interpretation) for the reliable classification of calcification and mass type breast abnormalities into malignant and...

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Bibliographic Details
Main Authors: Panchal, R., Verma, B.
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Breast cancer
Cancer detection
Feature extraction
Humans
Intelligent networks
Lesions
Mammography
Neural networks
Shape
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Summary:This paper investigates the significance of combining grey-level based image features and BI-RADS lesion descriptors along with patient age and a subtlety value (radiologists' interpretation) for the reliable classification of calcification and mass type breast abnormalities into malignant and benign classes. Three sets of experiments using grey-level based image features, BI-RADS features and combined features were conducted on DDSIM benchmark database. The classification rate 91% on mass dataset and 74% on calcification dataset was obtained when both types of features combined together.
ISSN:2161-4393
2161-4407
DOI:10.1109/IJCNN.2005.1556293