Breast cancer diagnosis using an efficient CAD system based on multiple classifiers

Ragab, Dina A. and Sharkas, Maha and Attallah, Omneya (2019) Breast cancer diagnosis using an efficient CAD system based on multiple classifiers. Diagnostics, 9 (4). 165. ISSN 2075-4418

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    Breast cancer is one of the major health issues across the world. In this study, a new computer-aided detection (CAD) system is introduced. First, the mammogram images are enhanced to increase the contrast. Second, the pectoral muscle is eliminated and the breast is suppressed from the mammogram. Afterward, some statistical features are extracted. Next, K-nearest neighbor (k14 NN) and Decision trees classifiers are used to classify the normal and abnormal lesions. Moreover, multiple classifier systems (MCS) is constructed as it usually improves the classification results. The MCS has two structures, cascaded and parallel structures. Finally, two wrapper feature selection (FS) approaches are applied to identify those features, which influence classification accuracy. Two data sets are combined together to test the CAD system proposed. The highest accuracy achieved with the proposed CAD system before FS was 99.7% using the Adaboost of the J48 decision tree classifiers. The highest accuracy after FS was 100 %, which was achieved with k–NN classifier. Moreover, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was equal to 1.0. The results showed that the proposed CAD system was able to accurately classify normal and abnormal lesions in mammogram samples.