@article{scholars1247,
            note = {cited By 110},
          volume = {34},
          number = {4},
             doi = {10.1016/j.compmedimag.2009.11.002},
           title = {Breast cancer diagnosis in digital mammogram using multiscale curvelet transform},
            year = {2010},
         journal = {Computerized Medical Imaging and Graphics},
           pages = {269--276},
            issn = {08956111},
          author = {Eltoukhy, M. M. and Faye, I. and Samir, B. B.},
        keywords = {Breast cancer diagnosis; Curvelet transforms; Digital mammogram; Digital mammograms; Multi-resolutions, Face recognition; Mammography; X ray screens, Feature extraction, article; breast cancer; cancer classification; cancer diagnosis; classifier; diagnostic accuracy; diagnostic procedure; digital mammography; human; image analysis; image processing; image reconstruction; priority journal, Algorithms; Breast Neoplasms; Female; Humans; Mammography; Radiographic Image Enhancement; Radiographic Image Interpretation, Computer-Assisted; Reproducibility of Results; Sensitivity and Specificity},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77952237386&doi=10.1016\%2fj.compmedimag.2009.11.002&partnerID=40&md5=a52910c5505a38495d14c0838f0c5aa6},
        abstract = {This paper presents an approach for breast cancer diagnosis in digital mammogram using curvelet transform. After decomposing the mammogram images in curvelet basis, a special set of the biggest coefficients is extracted as feature vector. The Euclidean distance is then used to construct a supervised classifier. The experimental results gave a 98.59 classification accuracy rate, which indicate that curvelet transformation is a promising tool for analysis and classification of digital mammograms. {\^A}{\copyright} 2009 Elsevier Ltd.}
}