Machine learning for evolutive lymphoma and residual masses recognition in whole body diffusion weighted magnetic resonance images.

Journal: Computer methods and programs in biomedicine
Published Date: Aug 4, 2021

Abstract

BACKGROUND: After the treatment of the patients with malignant lymphoma, there may persist lesions that must be labeled either as evolutive lymphoma requiring new treatments or as residual masses. We present in this work, a machine learning-based computer-aided diagnosis (CAD) applied to whole-body diffusion-weighted magnetic resonance images.

Authors

  • Radhia Ferjaoui
    University of Tunis El Manar, Research Laboratory of biophysics and Medical technologies (LRBTM), ISTMT, Tunis, 1006, Tunisia. Electronic address: radhiaferjeoui@gmail.com.
  • Mohamed Ali Cherni
    University of Tunis, LR13 ES03 SIME Laboratory, ENSIT, Montfleury 1008 Tunisia.
  • Sana Boujnah
    University of Tunis El Manar, National Engineering School of Tunis, Tunisia.
  • Nour El Houda Kraiem
    Hospital Aziza Othmana of Tunis, Radiology Department, Tunis, 1006, Tunisia.
  • Tarek Kraiem
    University of Tunis El Manar, Faculty of Medicine of Tunis, Tunis, 1007, Tunisia; University of Tunis El Manar, Research Laboratory of biophysics and Medical technologies (LRBTM), ISTMT, Tunis, 1006, Tunisia.

Keywords

External Resources

Popular Topics
Recent Journals