IJSRSET184881 | Received : 15May 2018 | Accepted :30May2018 |May-June-2018 [(4)8: 312-321] © 2018 IJSRSET | Volume 4 | Issue 8 | Print ISSN: 2395-1990 | Online ISSN : 2394-4099 Themed Section: Engineering and Technology 312 Breast Cancer Detection in Mammogram Using FuzzyC-Means And Random Forest Classifier Aleena Johny 1 , Jincy J Fernandez 2 1 M.Tech Scholar, Department of Computer Science and Engineering, Rajagiri School of Engineering and Technology Kakkanad, Kochi, India 2 Assistant Professor, Department of Computer Science and Engineering, Rajagiri School of Engineering and Technology Kakkanad, Kochi, India ABSTRACT Breast Cancer is one of the important reasons for death among ladies. Many research has been done on the diagnosis and detection of breast cancer using various image processing techniques. The proposed work deals with a technique for extracting the malignant masses in the mammography image for the earlier detection of breast cancer. The mammography images are complex, and also because of the noisy, inconsistent and incomplete data, several pre-processing techniques are used to enhance and make clear the targeted areas in the mammogram images. After segmenting the images into specific regions, based on its homogeneous characteristics, features are extracted which helps the classification more accurate. In this work, Fuzzy C-Means method is combined with Random Forest classifier to improve the accuracy. Keywords: Pre-Processing, Segmentation, Post-Processing, Random Forest Classifier, Fuzzy C-Means. I. INTRODUCTION Nowadays, the usage of image processing techniques in medical science are increasing day by day for the better diagnosis and treatment of a patient. Medical imaging helps in revealing the internal organs, which is useful for the medical practitioners to do laparoscopic surgeries for viewing body parts without opening the body. The development of various medical imaging methods such as CT, MRI, PET, [1] helps the physicians to find the disease affected area. Due to the inaccuracy of some image acquisition systems, noisy images are captured which affects the overall diagnosis of the patient. So pre-processing [2,3] plays a key role in image processing which improves the image quality by suppressing unwanted distortions in the captured image. Instead of processing the entire image which increases the complexity in terms of time and space, the image is divided into segments/parts based on few important characteristics [4]. The processing such as feature extraction are done after the extraction of region of interest (ROI). Breast cancer is a major cause of death among all cancers for women aged between 35 to 55 years and continues to be the leading cause of non- preventable cancer deaths. The proposed work deals with an approach for extracting the malignant masses in the mammography image for the detection of earlier breast cancer. The problem with mammography images are they are complex. Thus, image processing and feature extraction methods are used to assist radiologist for detecting tumour. Features extracted from suspicious regions in