Abstract
Mammography is a medical imaging technology that uses X-rays to depict the internal structure of breast reason. Mammograms generated through mammography process assist radiologists and doctors in the detection/diagnosis of breast diseases. A computer-aided diagnosis (CAD) system can be used for improving the diagnostic information using these mammograms. For the designing of an effective CAD system, texture features with machine learning play a vital role in the detection of breast cancer. This paper introduces a framework of hybrid texture features with an artificial neural network (ANN) for the accurate detection of breast cancer, where hybrid features are derived from combination of local binary patterns (LBP) and gray-level co-occurrence matrix (GLCM) features. From the experimental analysis on the mammography image analysis society (MIAS) database, it has been found that our proposed approach is significantly encouraging than other methods and well suited for the accurate diagnosis of breast cancer.
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