earticle

영어

Computer-aided diagnosis (CADx) is usually used as a second opinion by the radiologists. Improving CADx increases the treatment options and a cure is more likely. The main objective of this research is to introduce a new method for feature extraction in order to build a CADx model to discriminate between cancers, benign, and healthy parenchyma. For feature extraction, we use both human features, which are obtained by Digital Database for Screening Mammography (DDSM), and computational features, and we propose a new feature extraction method called Square Centroid Lines Gray Level Distribution Method (SCLGM). The experimental results are obtained from a data set of 410 images taken from DDSM for different types. Our method select 31 features from 145 extracted features; 18 of the selected features are from our proposed feature extraction method (SCLGM). We used both Receiver Operating Characteristics (ROC) and confusing matrix to measure the performance. In training stage, our proposed method achieved an overall classification accuracy of 96.3%, with 92.9% sensitivity and 94.3% specificity. In testing stage, our proposed method achieved an overall classification accuracy of 89%, with 88.6% sensitivity and 83.3% specificity.