Statistical Classification of Mammograms Using Random Forest Classifier

A woman in general has 12% chance of developing breast cancer and a 3.5% chance of dying from this disease, hence detection of cancer has received considerable attention in the recent years. Mammogram is an X-ray of the breast used to detect and diagnose breast cancer and other abnormalities. The ai...

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Hauptverfasser:	Vibha, L., Harshavardhan, G.M., Pranaw, K., Shenoy, P.D., Venugopal, K.R., Patnaik, L.M.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Breast cancer Cancer detection classification Classification tree analysis Decision trees digital mammography Diseases Educational institutions Image databases image mining Mammography Medical imaging Microprocessors Neoplasms Random forest
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creator	Vibha, L. Harshavardhan, G.M. Pranaw, K. Shenoy, P.D. Venugopal, K.R. Patnaik, L.M.
description	A woman in general has 12% chance of developing breast cancer and a 3.5% chance of dying from this disease, hence detection of cancer has received considerable attention in the recent years. Mammogram is an X-ray of the breast used to detect and diagnose breast cancer and other abnormalities. The aim of a screening mammogram is to detect a tumor that cannot be physically detected. This paper proposes a Decision Forest Classifier (DFC) for classifying mammograms. Results of screening the mammograms are organised by classification and finally grouped into three categories i.e., Normal, Benign and malign. Experimental results obtained indicate that the proposed method performs relatively well with the classification accuracy reaching nearly 90.45% in comparison with the already existing algorithms.
doi_str_mv	10.1109/ICISIP.2006.4286091
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Mammogram is an X-ray of the breast used to detect and diagnose breast cancer and other abnormalities. The aim of a screening mammogram is to detect a tumor that cannot be physically detected. This paper proposes a Decision Forest Classifier (DFC) for classifying mammograms. Results of screening the mammograms are organised by classification and finally grouped into three categories i.e., Normal, Benign and malign. Experimental results obtained indicate that the proposed method performs relatively well with the classification accuracy reaching nearly 90.45% in comparison with the already existing algorithms.</abstract><pub>IEEE</pub><doi>10.1109/ICISIP.2006.4286091</doi><tpages>6</tpages></addata></record>
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subjects	Breast cancer Cancer detection classification Classification tree analysis Decision trees digital mammography Diseases Educational institutions Image databases image mining Mammography Medical imaging Microprocessors Neoplasms Random forest
title	Statistical Classification of Mammograms Using Random Forest Classifier
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