Breast Cancer Detection on X-Tray Mammogram Images

Breast cancer (BC) is a common cancer affecting women everywhere in world. Mammography is identified and efficient technique to detect primary BC. The aim of project is for detect BC on mammogram in order to categorize disease by image processing when comparing with a previous technique. Through utilizing conventional methods, it creates it complex for radiology to detect cancer from patient's breast. In addition, there are environmental disturbances and technical problems if using the old method. Image processing techniques was separated to numerous elements. The elements are input, pre-processing, segmentation, morphological, object classification and classification. First pre-processing was done through Weiner and Median filter. Then, thresholding method on segmentation and finally, morphological will eliminate limitations at a segmentation. The image classified into 2 classes like normal and tumor. Both type of images analyzed based on elements. Additionally, it comprises a building of Graphical User Interface (GUI) which is utilized to generate the system as user-friendly. The developed model attain accuracy of 93.71 %, specificity of 82.53 % sensitivity of 94.36% for tumorous images.