@article { author = {Saeed, soran and Maaroof, Bestan and Shally, Alla}, title = {}, journal = {Journal of Garmian University}, volume = {5}, number = {1}, pages = {34-51}, year = {2018}, publisher = {University of Garmian}, issn = {23100087}, eissn = {25223879}, doi = {10.24271/garmian.302}, abstract = {}, keywords = {}, title_ku = {Mammography Detection System of Malignant breast mass Cancer Using Hybrid Expert System and Case Based Reasoning}, abstract_ku = {This research work, presents a computer-aided mammography detection of massimage for Malignant breast cancer a system has been developed to help radiologistsin order to increase diagnostic accuracy and called (ImageCBR). The aim of thiswork to find or detect similar Malignant image mass of breast cancer from baseknowledge by given a target one. similarity Generally, a ImageCBR system consistsof four stages: (a) preprocessing of the image (b) segmentation of regions of interest,such as a well-known mass breast features extraction and selection (shape, size,density, margin), and finally (c) image similarity (target and source). Theperformance evaluation metrics of ImageCBR systems are also reviewed.}, keywords_ku = {Case-Based Reasoning,Expert System,image processing,Image Similarity,breast cancer,mammography}, url = {https://jgu.garmian.edu.krd/article_66907.html}, eprint = {https://jgu.garmian.edu.krd/article_66907_cdbd54b46fab847650f7a00d4675e86e.pdf} }