TY - GEN
T1 - An integrated approach to Stage 1 breast cancer detection
AU - Fitzgerald, Jeannie M.
AU - Ryan, Conor
AU - Krawiec, Krzysztof
AU - Medernach, David
N1 - Publisher Copyright:
© 2015 Copyright held by the owner/author(s).
PY - 2015/7/11
Y1 - 2015/7/11
N2 - We present an automated, end-to-end approach for Stage 1 breast cancer detection. The first phase of our proposed work-flow takes individual digital mammograms as input and outputs several smaller sub-images from which the background has been removed. Next, we extract a set of features which capture textural information from the segmented images. In the final phase, the most salient of these features are fed into a Multi-Objective Genetic Programming system which then evolves classifiers capable of identifying those segments which may have suspicious areas that require further investigation. A key aspect of this work is the examination of several new experimental configurations which focus on textural asymmetry between breasts. The best evolved classifier using such a configuration can deliver results of 100% accuracy on true positives and a false positive per image rating of just 0.33, which is better than the current state of the art.
AB - We present an automated, end-to-end approach for Stage 1 breast cancer detection. The first phase of our proposed work-flow takes individual digital mammograms as input and outputs several smaller sub-images from which the background has been removed. Next, we extract a set of features which capture textural information from the segmented images. In the final phase, the most salient of these features are fed into a Multi-Objective Genetic Programming system which then evolves classifiers capable of identifying those segments which may have suspicious areas that require further investigation. A key aspect of this work is the examination of several new experimental configurations which focus on textural asymmetry between breasts. The best evolved classifier using such a configuration can deliver results of 100% accuracy on true positives and a false positive per image rating of just 0.33, which is better than the current state of the art.
KW - Classification
KW - Mammography
KW - Multi-Objective Genetic Programming
UR - http://www.scopus.com/inward/record.url?scp=84963704553&partnerID=8YFLogxK
U2 - 10.1145/2739480.2754761
DO - 10.1145/2739480.2754761
M3 - Conference contribution
AN - SCOPUS:84963704553
T3 - GECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference
SP - 1199
EP - 1206
BT - GECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference
A2 - Silva, Sara
PB - Association for Computing Machinery, Inc
T2 - 16th Genetic and Evolutionary Computation Conference, GECCO 2015
Y2 - 11 July 2015 through 15 July 2015
ER -
