Search for Resolution Invariant Wavelet Features of Melanoma Learned by a Limited ANN Classifier

Grzegorz Surówka

Abstrakt
This article addresses the Computer Aided Diagnosis (CAD) of
melanoma pigmented skin cancer. We present back-propagated Artificial Neural
Network (ANN) classifiers discriminating dermoscopic skin lesion images into
two classes: malignant melanoma and dysplastic nevus. Features used for our
classification experiments are derived from wavelet decomposition coefficients
of the image. Our research objective is i) to select the most efficient topology
of the hidden layers and the network learning algorithm for full-size and
downgraded image resolutions and, ii) to search for resolution-invariant topologies
and learning methods. The analyzed classifiers should be fit to work on
ARM-based hand-held devices, hence we take into account only limited learning
setups.
Słowa kluczowe: melanoma, CAD, wavelets, ANN
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