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Advisor(s)
Abstract(s)
In the field of breast cancer research, and more than
ever, new computer aided diagnosis based systems have been
developed aiming to reduce diagnostic tests false-positives.
Within this work, we present a data mining based approach
which might support oncologists in the process of breast cancer
classification and diagnosis. The present study aims to
compare two breast cancer datasets and find the best methods
in predicting benign/malignant lesions, breast density classification,
and even for finding identification (mass /
microcalcification distinction). To carry out these tasks, two
matrices of texture features extraction were implemented
using Matlab, and classified using data mining algorithms,
on WEKA. Results revealed good percentages of accuracy
for each class: 89.3 to 64.7 % - benign/malignant; 75.8 to
78.3 % - dense/fatty tissue; 71.0 to 83.1 % - finding identification.
Among the different tests classifiers, Naive Bayes was
the best to identify masses texture, and Random Forests was
the first or second best classifier for the majority of tested
groups.
Description
Keywords
Breast cancer diagnosis Features extraction Data mining techniques
Citation
Publisher
Springer Verlag