Alpaslan N.Kara A.Zencir B.Hanbay D.2024-08-042024-08-0420159781467373869https://doi.org/10.1109/SIU.2015.7130121https://hdl.handle.net/11616/922852015 23rd Signal Processing and Communications Applications Conference, SIU 2015 -- 16 May 2015 through 19 May 2015 -- 113052Breast cancer is one of the most deadly diseases for women. Mammogram is very important imaging technique used diagnosis in early stages of breast cancer. In this study, a decision support system which helps experts to examine mammogram images in the fight against breast cancer is developed. In this study, firstly several preprocesses are applied to mammogram to make image clear and segmentation of mass is provided with an appropriate threshold value. After the segmentation processes, features of the tumor mass are obtained. The obtained features are classified as normal, benign or malignant using kNN (k-nearest neighbours) classifiers. In this study, its have been were shown that, effect of kurtosis, skewness and wavelet energy features on classification performance is shown. As a result, it has been seen that, these features improve the classification performance. © 2015 IEEE.trinfo:eu-repo/semantics/closedAccessClassification (of information)Decision support systemsDiseasesHigher order statisticsImage classificationImage segmentationMammographyNearest neighbor searchX ray screensBreast CancerBreast massClassification performanceK-nearest neighboursMammogram imagesSegmentation processThreshold-valueWavelet energy featureMedical imagingConference Object1469147210.1109/SIU.2015.71301212-s2.0-84939208229N/A