Asian Science Citation Index is committed to provide an authoritative, trusted and significant information by the
coverage of the most important and influential journals
to meet the needs of the global scientific community.
In the present day, the most rampant cancer discovered among women in various parts of the world
is breast cancer. Early detection and diagnosis of breast cancer which can be achieved through mammography
increases treatment options and a cure is more likely. In order to diagnose breast cancer, radiologists carefully
examine patient’s X-ray images of the breast (mammograms) to see if there are significant visually extractable
features that indicate the presence of breast cancer. However, visual features are subjective and diagnostic
decisions should not be based on them because they are a function of radiologist’s opinion and experience.
Thus, to eliminate the differential interpretations of abnormalities seen on mammograms among radiologists it
is expedient to use computers to aid the extraction and selection of features which are not necessarily visually
extractable. This study makes use of patient’s mammograms acquired from Radiology Department, Obafemi
Awolowo University Teaching Hospital Complex Ile-Ife, Nigeria. Features are extracted from the mammograms
using feature descriptors from Gray Level Co-occurrence Matrix (GLCM) and most discriminating features are
selected using the proposed hybrid feature selection algorithm which is implemented to improve the
classification accuracy. For each of the input image, the algorithm automatically selects relevant features from
the set of extracted features. This algorithm reduces the extracted features by selecting the most relevant
features thereby finding (near) optimal classification model of breast mammographic images. Two methods are
combined for selecting optimal features viz.: the sequential forward selection and the Genetic algorithm. This
is done, so as to cover the disadvantages of each one by the advantages of the other.
