2016 · 145 citations · 11 references
ComputingComputer ScienceRadiologyComputer VisionDiagnostic ImagingMachine LearningBreast ImagingFeature DetectionImage Feature EngineeringMedical Image AnalysisPattern RecognitionSupervised LearningAccuracyImage Signal ProcessingLogistic RegressionMammographyNeural Network ModelingPattern ExtractionScreening MammographyTumor Detection In Mammography
Breast cancer is very popular between females all over the world. However, detecting this cancer in its first stages helps in saving lives. Radiologists can predict if the mammography images have cancer or not, but they may miss about 15% of them. In this paper, we propose a new method to detect the breast cancer with high accuracy. This method consists of two main parts, in the first part the image processing techniques are used to prepare the mammography images for feature and pattern extraction process. The second part is presented by utilizing the extracted features as an input for a two types of supervised learning models, which are Back Propagation Neural Network (BPNN) model and the Logistic Regression (LR) model. In this paper we examined the accuracy of these models. The results showed that the LR model utilized more features than the BPNN.
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