Barstu?an M.Ceylan R.2020-03-262020-03-2620179.78154E+12https://dx.doi.org/10.1109/IDAP.2017.8090283https://hdl.handle.net/20.500.12395/357022017 International Artificial Intelligence and Data Processing Symposium, IDAP 2017 -- 16 September 2017 through 17 September 2017 -- 115012Dictionary Learning is a method used in signal and image processing. In this study, classification of mammogram images were realized by using dictionary learning and sparse representation algorithms. The attributes of the images were detected with Wavelet Transform and PCA, and the new dataset which was created by the obtained attributes were classified by Dictionary Learning. Moreover, the classification performance of the Dictionary Learning algorithm was evaluated by classifying the new dataset with SVM, Rotation Forest and AdaBoost algorithms. The best classification accuracy was obtained by PCA-Dictionary Learning algorithm as 98.89%. © 2017 IEEE.tr10.1109/IDAP.2017.8090283info:eu-repo/semantics/closedAccessClassificationDictionary learningPrincipal component analysisSparse representationWavelet transformConference ObjectN/A