Ragab Dina A, Sharkas Maha, Attallah Omneya
Electronics and Communications Engineering Department, Arab Academy for Science, Technology, and Maritime Transport (AASTMT), Alexandria 1029, Egypt.
Electronic and Electrical Engineering Department, University of Strathclyde, Glasgow G1 1XW, UK.
Diagnostics (Basel). 2019 Oct 26;9(4):165. doi: 10.3390/diagnostics9040165.
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.
乳腺癌是全球主要的健康问题之一。在本研究中，引入了一种新的计算机辅助检测（CAD）系统。首先，对乳腺X线图像进行增强以提高对比度。其次，去除胸肌并从乳腺X线图像中抑制乳房部分。之后，提取一些统计特征。接下来，使用k近邻（k-NN）和决策树分类器对正常和异常病变进行分类。此外，构建了多分类器系统（MCS），因为它通常能提高分类结果。MCS有两种结构，级联结构和并行结构。最后，应用两种包装特征选择（FS）方法来识别那些影响分类准确性的特征。将两个数据集（1）乳腺X线图像分析协会数字乳腺X线数据库（MIAS）和（2）数字乳腺X线摄影梦想挑战数据集合并在一起，以测试所提出的CAD系统。在使用J48决策树分类器的Adaboosting方法时，所提出的CAD系统在FS之前达到的最高准确率为99.7%。FS之后的最高准确率为100%，这是使用k-NN分类器实现的。此外，接收器操作特征（ROC）曲线的曲线下面积（AUC）等于1.0。结果表明，所提出的CAD系统能够准确地对乳腺X线图像样本中的正常和异常病变进行分类。
