Authors: Laxman Singh; Zainul Abdin Jaffery
Addresses: Department of Electronics & Communication Engineering, Noida Institute of Engineering and Technology, Greater Noida 201306, India ' Department of Electrical Engineering, Jamia Millia Islamia, Jamia Nagar, New Delhi 110025, India
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%.
Keywords: breast cancer; mammograms; artificial neural network; principal component analysis.
International Journal of Biomedical Engineering and Technology, 2018 Vol.27 No.3, pp.233 - 246
Received: 30 Apr 2016
Accepted: 26 Aug 2016
Published online: 06 Aug 2018 *