Computer-Aided Detection and Classification of Masses in Digitized Mammograms Using Artificial Neural Network

Abstract

AbstractIn this paper we present a computer aided diagnosis (CAD) system for mass detection and classification in digitized mammograms, which performs mass detection on regions of interest (ROI) followed by the benign-malignant classification on detected masses. In order to detect mass effectively, a sequence of preprocessing steps are proposed to enhance the contrast of the image, remove the noise effects, remove the x-ray label and pectoral muscle and locate the suspicious masses using Haralick texture features generated from the spatial gray level dependence (SGLD) matrix. The main aim of the CAD system is to increase the effectiveness and efficiency of the diagnosis and classification process in an objective manner to reduce the numbers of false-positive of malignancies. Artificial neural network (ANN) is proposed for classifying the marked regions into benign and malignant and 83.87% correct classification for benign and 90.91% for malignant is achieved.KeywordsMammogramsArtificial Neural NetworkRegion GrowingHaralick Texture Features