Breast cancer diagnosis system using hybrid support vector machine-artificial neural network

Tze Sheng Lim, Kim Gaik Tay, Audrey Huong, Xiang Yang Lim


Breast cancer is the second most common cancer occurring in women. Early detection through mammogram screening can save more women’s lives. However, even senior radiologists may over-diagnose the clinical condition, which causes unnecessary biopsies or missed masses that may endanger one’s life. Machine Learning (ML), which encompasses a wide range of methods and algorithms, is the most used technique in the diagnosis of cancer to help reduce human errors. It is, therefore, the aim of this study to develop a computer-aided detection (CAD) systems using ML for classification purposes. In this work, 80 digital mammograms of normal breasts, 40 of benign and 40 of malignant cases were chosen from the mini MIAS dataset. These images were denoised using median filter after they were segmented to obtain a region of interest (ROI) and enhanced using histogram equalization. This work compared the performance of Artificial Neural Network (ANN), Support Vector Machine (SVM), reduced features of SVM and the hybrid SVM-ANN for classification process using the statistical and Gray Level Co-occurrence Matrix (GLCM) features extracted from the enhanced images. It is found that the hybrid SVM-ANN gives the best accuracy of 99.4 % and 100 % in differentiating normal from abnormal, and benign from malignant cases, respectively. This hybrid SVM-ANN model was deployed in developing the CAD system. The performance of the developed CAD system was further tested with a new set of 100 images not involved in the training phase, which showed relatively good accuracy of 98 %.


ANN; breast cancer; CAD; classification; SVM;

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578