A Two-Stage Model for Breast Cancer Detection and Classification from Mammograms

Abstract

Breast cancer is a type of disease which is common to women. It is an abnormal behaviour which hinders the functionality of the normal breast cells which leads to forming a tumor in the form of lump. Early detection of breast cancers tends to reduce death risk and provides a chance of a suitable and better treatment. A two-stage classification model was proposed to detect and classify breast cancer using two publicly available datasets namely MIAS and the DDSM dataset. The first classification stage uses Convolution Neural Network (CNN) to classify the mammogram image as either normal or abnormal while K-Nearest Neighbour (KNN) was used for the second classification stage to classify the abnormal image into benign or malignant. The features used for the second stage classification were extracted using Gray Level Co-occurrence Matrix (GLCM), which was later used for classification. The performance was evaluated based on accuracy, sensitivity, specificity, precision, False Positive Rate (FPR), F1 score and Matthews Correlation Coefficient (MCC). The results show that CNN obtained the highest accuracy, sensitivity, specificity, precision, FPR, F1 score and MCC of 99.03%, 0.9831, 1.0000, 1.0000, 0.0000, 0.9915 and 0.9804 respectively, while KNN has an accuracy, sensitivity, specificity, precision, FPR, F1 score and MCC of 76.27%, 0.7667, 0.7586, 0.7667, 0.2414, 0.7667 and 0.5253 respectively.