Computer aided detection of mammographic mass using exact Gaussian–Hermite moments

  • Mohamed Meselhy Eltoukhy
  • Mohamed Elhoseny
  • Khalid M. Hosny
  • Amit Kumar Singh
Original Research


Breast cancer is one of the common cancer deaths in women worldwide. Early detection is the key to reduce the mortality rate. Clinical trials have shown that computer aided systems (CAD) have improved the accuracy of breast cancer detection. This paper proposed a highly accurate CAD system based on extracting highly significant features using exact Gaussian–Hermite moments. The obtained feature vector is presented to K-NN, random forests and AdaBoost classifiers. The proposed system is evaluated using two different datasets namely IRMA and MIAS. The evaluation metrics of accuracy, TP, FP and area under ROC curve using 10-fold cross-validation are calculated. The results indicate the usefulness of the proposed exact Gaussian–Hermite moments features for distinguishing between normal and abnormal lesions and the superiority of the moments features compared with the conventional methods.


Computer aided diagnose Gaussian–Hermite Breast cancer Health informatics 



The IRMA dataset used in this study was used by courtesy of Thomas M. Deserno, Department of Medical Informatics, Aachen, Germany. In addition, we would like to thank Dr. Mohamed Tahoun and Dr. S. J. Gardezi for their discussion and invaluable comments.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computer Science Department, Faculty of Computers and InformaticsSuez Canal UniversityIsmailiaEgypt
  2. 2.Faculty of Computers and InformationMansoura UniversityEl MansûraEgypt
  3. 3.Department of Information Technology, Faculty of Computers and InformaticsZagazig UniversityZagazigEgypt
  4. 4.Jaypee University of Information Technology (JUIT)SolanIndia

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