This paper is published in Volume-3, Issue-2, 2017
Area
Breast Cancer
Author
Chandana Saipriya. V, Dhanalakshmi. B, Gnanasoundari. S, Mercy Therasa. M, Hemadevi. J
Org/Univ
JEPPIAAR SRR Engineering College, Tamil Nadu, India
Pub. Date
06 April, 2017
Paper ID
V3I2-1450
Publisher
Keywords
Active Contour Segmentation, K-Means Algorithm, Expectation Maximization, Principle Component Analysis, Random Forest Classification.

Citationsacebook

IEEE
Chandana Saipriya. V, Dhanalakshmi. B, Gnanasoundari. S, Mercy Therasa. M, Hemadevi. J. Automatic Mammogram Tumor Detection Using Supervised Learning Method, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Chandana Saipriya. V, Dhanalakshmi. B, Gnanasoundari. S, Mercy Therasa. M, Hemadevi. J (2017). Automatic Mammogram Tumor Detection Using Supervised Learning Method. International Journal of Advance Research, Ideas and Innovations in Technology, 3(2) www.IJARIIT.com.

MLA
Chandana Saipriya. V, Dhanalakshmi. B, Gnanasoundari. S, Mercy Therasa. M, Hemadevi. J. "Automatic Mammogram Tumor Detection Using Supervised Learning Method." International Journal of Advance Research, Ideas and Innovations in Technology 3.2 (2017). www.IJARIIT.com.

Abstract

Breast cancer is the most occupied type of cancer in women that caused the most deaths among women. The early detection of breast cancer is more important for the chances of survival of the patient. This work has mainly four modules: Pre-processing, Segmentation is carried out by Active Contour algorithm and Advanced K-means algorithm, Feature extraction is done by Gray Level Co-occurrence Matrix (GLCM), Expectation Maximization (EM) and Principle Component Analysis (PCA), finally classification is done by Random Forest Classification. To achieve the objective of this work, MIAS (Mammographic Image Analysis Society) and IN breast databases are used as input images. The Accuracy achieved in this system is 95.83%.
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