Breast Cancer Detection in Mammogram Using Fuzzy C-Means And Random Forest Classifier

Authors(2) :-Aleena Johny, Jincy J Fernandez

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 speci?c regions, based on its homogeneous characteristics, features are extracted which helps the classi?cation more accurate. In this work, Fuzzy C-Means method is combined with Random Forest classi?er to improve the accuracy.

Authors and Affiliations

Aleena Johny
M.Tech Scholar, Department of Computer Science and Engineering, Rajagiri School of Engineering and Technology Kakkanad, Kochi, India
Jincy J Fernandez
Assistant Professor, Department of Computer Science and Engineering, Rajagiri School of Engineering and Technology Kakkanad, Kochi, India

Pre-Processing, Segmentation, Post-Processing, Random Forest Classi?er, Fuzzy C-Means.

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Publication Details

Published in : Volume 4 | Issue 8 | May-June 2018
Date of Publication : 2018-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 312-321
Manuscript Number : IJSRSET184881
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Aleena Johny, Jincy J Fernandez, " Breast Cancer Detection in Mammogram Using Fuzzy C-Means And Random Forest Classifier, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 8, pp.312-321, May-June-2018. Citation Detection and Elimination     |     
Journal URL : https://ijsrset.com/IJSRSET184881

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