A Review on Textural Features Based Computer Aided Diagnostic System for Mammogram Mass Classification Using GLCM & RBFNN
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2014 by IJETT Journal|
|Year of Publication : 2014|
|Authors : NehaTripathi, Supriya P Panda
|DOI : 10.14445/22315381/IJETT-V17P290|
NehaTripathi, Supriya P Panda "A Review on Textural Features Based Computer Aided Diagnostic System for Mammogram Mass Classification Using GLCM & RBFNN", International Journal of Engineering Trends and Technology (IJETT), V17(9),462-464 Nov 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Computer Aided Diagnosis (CAD) is used as second opinion forthe Radiologist and used as a solution in detection of breast cancer.It already proved its success in the reduction of human error in reading the mammogram images, and their better and reliable classification into benign and malignant masses. This paper proposed an algorithm for the detection of breast cancer in early stage, textural feature analysis proves to be one of the competent step for the detection of abnormalities. This paper estimate Gray-Level-Co-Occurrence Matrix (GLCM) method for extraction of textural feature from the segmented mammograms,and Radial Basis Function Neural Network (RBFNN) is taken as classifier and its performance evaluation is done with different textural features. The objective of this paper is to find a clear classification of breast cancer in early stage.
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Mammogram, Breast cancer, Textural Feature, GLCM, RBFNN,CAD, ANN.