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Open AccessArticle

Breast Cancer Diagnosis Using an Efficient CAD System Based on Multiple Classifiers

1
Electronics and Communications Engineering Department, Arab Academy for Science, Technology, and Maritime Transport (AASTMT), Alexandria 1029, Egypt
2
Electronic and Electrical Engineering Department, University of Strathclyde, Glasgow G1 1XW, UK
*
Author to whom correspondence should be addressed.
Diagnostics 2019, 9(4), 165; https://doi.org/10.3390/diagnostics9040165
Received: 4 September 2019 / Revised: 21 October 2019 / Accepted: 24 October 2019 / Published: 26 October 2019
(This article belongs to the Special Issue Multimodality Breast Imaging)
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 were enhanced to increase the contrast. Second, the pectoral muscle was eliminated and the breast was suppressed from the mammogram. Afterward, some statistical features were extracted. Next, k-nearest neighbor (k-NN) and decision trees classifiers were used to classify the normal and abnormal lesions. Moreover, multiple classifier systems (MCS) was constructed as it usually improves the classification results. The MCS has two structures, cascaded and parallel structures. Finally, two wrapper feature selection (FS) approaches were applied to identify those features, which influence classification accuracy. The two data sets (1) the mammographic image analysis society digital mammogram database (MIAS) and (2) the digital mammography dream challenge were combined together to test the CAD system proposed. The highest accuracy achieved with the proposed CAD system before FS was 99.7% using the Adaboosting 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. View Full-Text
Keywords: the computer-aided detection; the pectoral muscle removal; the statistical features; the decision trees; the k-nearest neighbor; feature selection the computer-aided detection; the pectoral muscle removal; the statistical features; the decision trees; the k-nearest neighbor; feature selection
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Ragab, D.A.; Sharkas, M.; Attallah, O. Breast Cancer Diagnosis Using an Efficient CAD System Based on Multiple Classifiers. Diagnostics 2019, 9, 165.

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