Mammogram classification using Extreme Learning Machine and Genetic Programming
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- @InProceedings{Menaka:2014:ICCCI,
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author = "K. Menaka and S. Karpagavalli",
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booktitle = "International Conference on Computer Communication and
Informatics (ICCCI 2014)",
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title = "Mammogram classification using Extreme Learning
Machine and Genetic Programming",
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year = "2014",
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month = jan,
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abstract = "Mammogram is an x-ray examination of breast. It is
used to detect and diagnose breast disease in women who
either have breast problems such as a lump, pain or
nipple discharge as well as for women who have no
breast complaints. Digitised mammographic image is
analysed for masses, calcifications, or areas of
abnormal density that may indicate the presence of
cancer. Automated systems to analyse and classify the
mammogram images as benign or malignant will drive the
medical experts to take timely clinical decision. In
this work, the mammogram classification task carried
out using powerful supervised classification techniques
namely Extreme Learning Machine with kernels like
linear, polynomial, radial basis function and Genetic
Programming. The various task involved in this work are
image preprocessing, feature extraction, building
models through training and testing the classifier. The
two types of mammogram image, Benign and Malignant are
considered in this work and 50 images for each type
collected from Mini MIAS database. Selection of Region
of Interest (ROI) from the original image and Adaptive
Histogram Enhancement are applied on the mammogram
image before extracting the intensity histogram and
gray level co-occurrence matrix features. In the
dataset, for training 80percent of the data are used
and for testing 20percent of data are used. Models are
built using Extreme Learning Machine and Genetic
Programming. The performances of the models are tested
with test dataset and the results are compared. The
predictive accuracy and training time of the classifier
Genetic Programming is substantially better than the
classifier built using Extreme Learning Machine with
kernels linear, polynomial and radial basis function.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/ICCCI.2014.6921724",
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notes = "Also known as \cite{6921724}",
- }
Genetic Programming entries for
K Menaka
S Karpagavalli
Citations