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|Title||Mammogram Computer-Aided Diagnosis|
Computer-aided diagnosis (CADx) is used to help radiologists in interpretation mammograms and 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 enhance and introduce a new method for feature extraction and selection 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. For computational feature extraction, we enhance and use two pre-existed feature extraction methods, which are the Run Difference Method (RDM) and the Spatial Gray Level Dependence Method (SGLDM), and we propose a new feature extraction method called Square Centroid Lines Gray Level Distribution Method (SCLGM). Then, we evaluate and introduce a new method for feature selection by running both of forward sequential and genetic algorithm search methods individually. Later we evaluate the results. 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.
|Publisher||the islamic university|
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