Multi-class classification of mammograms with hesitancy based Hanman transform classifier on pervasive information set texture features

Abstract

With an aim to enhance the survival rate of women from breast cancer, this paper extends the Gabor, wavelet, and structure-based information set features into the pervasive information set features for more accurate breast cancer diagnosis from mammograms. The notion of a pervasive information set arises from expanding the information set to incorporate the intuitionistic fuzzy set. The effectiveness of the proposed features is evaluated on an annotated private dataset acquired from Superspeciality Cancer Hospital, New Delhi, and the public datasets comprising Digital Database for Screening Mammography, INbreast database, and the Mammographic Image Analysis Society database to validate their efficacy for multi-class categorization of mammograms employing the Hesitancy-based Hanman transform classifier. This classifier not only embodies the uncertainties in the errors between the training and test features but also typifies the deficiencies in the modelling of the membership function. The results of the Analysis of variance test confirm that the proposed features are statistically relevant and the experimental outcomes verified by expert radiologists validate the clinical significance of the proposed work. The proposed methods inhibiting the “Non-data hungry” aspect give 100% accuracy for multi-class classification on both public and private datasets which are higher than those obtained by the state of art methods available in the literature. These results will aid the radiologists in the early diagnosis of breast cancer.