Automatic Detection and Classification of Masses in Digital Mammograms
Shankar Thawkar, Ranjana Ingolikar · International journal of intelligent engineering and systems · 2016
Breast Cancer is still one of the leading cancers in women.Mammography is the best tool for early detection of breast cancer.In this work methods for automatic detection and classification of masses into benign or malignant has been proposed.The suspicious masses are detected automatically by performing image segmentation with Otsu's global thresholding technique, morphological operations and watershed transformation.Twenty-five features based on intensity, texture and shape are extracted from each of the 651 mammograms obtained from Database of Digitized Screen-film Mammograms.The Eight most significant features selected by step-wise Linear Discriminate Analysis are used to classify masses using Fisher's Linear Discriminate Analysis, Support Vector Machine and Multilayer Perceptron with two training algorithms Levenberg-Marquardt and Bayesian Regularization.The performance evaluation of classifiers indicates that MLP is better than both LDA and SVM.MLP-RBF has 98.9% accuracy with area under Receiver Operating Characteristics curve AZ=0.98±0.007,MLP-LM 96.0% accuracy with AZ=0.97±0.007,SVM 91.4% accuracy with AZ=0.956±0.009and LDA 90.3% accuracy with AZ=0.956±0.009.All the results achieved are promising when compared with some existing work.