Elm Based Detection of Micro-Calcification in Mammogram using Glcm Features
Jayesh George Melekoodappattu1, Perumal Sankar Subbian2
1Jayesh George Melekoodappattu, Department of Electronics and Communication Engineering, Vimal Jyothi Engineering College, Kannur, India.
2Perumal Sankar Subbian, Department of Electronics and Communication Engineering, TocH Institute of Science and Technology, Ernakulam, India.

Manuscript received on 10 April 2019 | Revised Manuscript received on 16 May 2019 | Manuscript published on 30 May 2019 | PP: 1146-1151 | Volume-8 Issue-1, May 2019 | Retrieval Number: A3198058119/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The breast is made up of many different types of tissue and cells. When the cells in the breast grow or change abnormally and it is called breast cancer. Most breast cancers occur in women who are over the age of fifty. Calcification is the main causes of breast cancer. The deposit of calcium in breast tissue is known as calcification. And it is two types, Micro- calcification and macro-calcification. Large calcium deposits represent the macro-calcification which may relate to non-cancerous. The tiny white dots on mammogram represent the micro-calcification which is the earliest stage of breast cancer and the calcification can be found in different shapes. Mammography is the one of the method to determine the breast cancer. In this paper we are determining the micro- calcification in mammogram using different steps which include preprocessing, enhancement, feature extraction, feature selection and the classification.
Index Terms: Extreme Learning Machine, Global Swarm Optimization, Gray level Co- Occurrence matrix, Mammography.
Scope of the Article: Machine Learning