Mammogram classification using Extreme Learning Machine and Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "K. Menaka and S. Karpagavalli",
  booktitle =    "International Conference on Computer Communication and
                 Informatics (ICCCI 2014)",
  title =        "Mammogram classification using Extreme Learning
                 Machine and Genetic Programming",
  year =         "2014",
  month =        jan,
  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.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/ICCCI.2014.6921724",
  notes =        "Also known as \cite{6921724}",

Genetic Programming entries for K Menaka S Karpagavalli