Abstract
Breast cancer continues to be a major health problem in the world. Detection of breast cancer at an early stage can reduce the mortality rate in women. Calcifications and masses are treated as the early sign of breast cancer. However, it is difficult to distinguish mass regions from surrounding tissues due to their low contrast and ambiguous margins and their classification is even more challenging. This paper presents a computer-aided diagnosis (CAD) system to classify the masses into benign and malignant using artificial neural network (ANN). The gray level and texture features are used as an input to the ANN. The proposed system achieved the sensitivity of 92.6% and specificity of 93.3% with a classification accuracy of 92.9%.
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