MRI Breast cancer diagnosis approach using adaptive Ant-based segmentation and Multilayer Perceptron NN classifier

Citation:
M.Moftah, A. E. Hassanien, A. Taher, and M. Shoman, "MRI Breast cancer diagnosis approach using adaptive Ant-based segmentation and Multilayer Perceptron NN classifier", Applied Soft Computing, Elsiever, vol. 14, issue Part A, pp. 62-71, 2014.

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This article introduces a hybrid approach that combines the advantages of
fuzzy sets, ant-based clustering and Bayesian classifier, in conjunction with
statistical-based feature extraction technique. An application of breast cancer
MRI imaging has been chosen and hybridization system has been applied
to see their ability and accuracy to classify the breast cancer images into
various outcomes: normal or non-normal. The introduced hybrid system
starts with an algorithm based on type-II fuzzy sets to enhance the contrast
of the input images. This is followed by an improved version of the classical
ant-based clustering algorithm, called adaptive ant-based clustering to identify
target objects through an optimization methodology that maintains the
optimum result during iterations. Then, more than twenty statistical-based
features are extracted and normalized. Finally, a Bayesian classifier was employed
to evaluate the ability of the lesion descriptors for discrimination of
different regions of interest to determine whether they represent cancer or
not. To evaluate the performance of presented approach, we present tests on
different breast MRI images. The experimental results obtained, show that
the adaptive ant-based segmentation is superior to the classical ant-based
clustering technique and the overall accuracy offered by the employed hybrid
technique confirm that the effectiveness and performance of the proposed
hybrid system is high

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