Classification of digital mammograms using information set features and Hanman Transform based classifiers
Jyoti Dabass, Madasu Hanmandlu, Rekha Vig · Informatics in Medicine Unlocked · 2020
Several studies have been made in the literature for the early detection of breast cancer using mammograms. These studies mainly deal with methods that do not capture local information. To fill up this gap this paper presents an approach that extracts the local features called information set features representing the uncertainties in the distributions of grey levels in windows/sub-images of a mammogram using the Mamta-Hanman entropy function. The extracted features are used for classification into two-class (abnormal, normal) and three-class (normal, benign, malignant) modes. Two classifiers are used, the first is the Hanman transform classifier that represents the uncertainties in the error vectors between training feature vectors of a patient and the test feature vector of an unknown patient, and the second is hesitancy based Hanman transform classifier that not only represents the uncertainties in the error vectors but also the deficiencies in the modeling of membership and non-membership functions. Both classifiers outperform the methods considered for comparison on the same mini-MIAS database.