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Image cytometry data from breast lesions analyzed using hybrid networks

Lookup NU author(s): Dr Gajanan Sherbet


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The treatment and therapy to be administered on breast cancer patients are dependent on the stage of the disease at time of diagnosis. It is therefore crucial to determine the stage at the earliest time possible. Tumor dissemination to axillary lymph nodes has been regarded as an indication of tumor aggression, thus the stage of the disease. Neural networks have been employed in many applications including breast cancer prognosis. The performance of the networks have often been quoted based on accuracy and mean squared error. In this paper, the performance of hybrid networks based on Multilayer Perceptron and Radial Basis Function networks to predict axillary lymph node involvement have been investigated. A measurement of how confident the networks are with respect to the results produced is also proposed. The input layer of the networks include four image cytometry features extracted from fine needle aspiration of breast lesions. The highest accuracy achieved by the hybrid networks was 69% only. However, most of the correctly predicted cases had a high confidence level. © 2005 IEEE.

Publication metadata

Author(s): Sakim HAM, Isa NAM, Naguib RNG, Sherbet GV

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Annual International Conference of the IEEE Engineering in Medicine and Biology

Year of Conference: 2005

Pages: 2059-2062

Publisher: IEEE


DOI: 10.1109/IEMBS.2005.1616863

Library holdings: Search Newcastle University Library for this item

ISBN: 0780387406