Detection of Normal Mammograms based on Breast Tissue Density using GLCM Features

Mona Y. Elshinawy, Abdel-Hameed A. Badawy, Wael W. Abdelmageed, and Mohamed F. Chouikha


Breast Tissue Density, Computer Aided Detection and Diagnosis, Classification, GLCM


Breast cancer is the second leading cause of cancer-related deaths in women in the US. Two main problems appear to affect the decision of detecting and diagnosing breast cancer:the accuracy of the CAD systems used, and the radiologists’ performance in reading and diagnosing mammograms.In this work we aim to improve CAD system’s performance by adding a preprocessing step to reduce the false negative rate significantly. We propose to divide mammograms into two distinct categories according to tissue type(fatty, and dense). A one-class classifier is used for each tissue-type separately to enhance the performance of the overall classification task. GLCM features are extracted for each of dense and fatty mammograms. The sensitivity for each tissue type was improved significantly (~100%)when used separately compared to the sensitivity of existing systems (90%) that uses all mammograms regardless of tissue type.

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