Open AccessClassification of mammogram using two‐dimensional discrete orthonormal S‐transform for breast cancer detection
Author(s) -
Beura Shradhananda,
Majhi Banshidhar,
Dash Ratnakar,
Roy Susnata
Publication year - 2015
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl.2014.0108
Subject(s) - pattern recognition (psychology) , orthonormal basis , artificial intelligence , adaboost , receiver operating characteristic , mammography , computer science , random forest , cross validation , mathematics , contextual image classification , classifier (uml) , feature selection , breast cancer , image (mathematics) , machine learning , cancer , medicine , physics , quantum mechanics
An efficient approach for classification of mammograms for detection of breast cancer is presented. The approach utilises the two‐dimensional discrete orthonormal S‐transform (DOST) to extract the coefficients from the digital mammograms. A feature selection algorithm based the on null‐hypothesis test with statistical ‘two‐sample t ‐test’ method has been suggested to select most significant coefficients from a large number of DOST coefficients. The selected coefficients are used as features in the classification of mammographic images as benign or malignant. This scheme utilises an AdaBoost algorithm with random forest as its base classifier. Two standard databases Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) are used for the validation of the proposed scheme. Simulation results show an optimal classification performance with respect to accuracies of 98.3 and 98.8% and AUC (receiver operating characteristic) values of 0.9985 and 0.9992 for MIAS and DDSM, respectively. Comparative analysis shows that the proposed scheme outperforms its competent schemes.