On Monday, August 12, 2024, the master's thesis defense of the student Rabab Jawad Kazem from the Physiology and Medical Physics Department was completed. Her thesis is titled: "Automatic Classification of Breast Masses in Dynamic Contrast-Enhanced Magnetic Resonance Imaging using Kinetic Curves and Deep Learning Features." **Objective:** The study aims to design a new system to distinguish between benign and malignant masses in DCE-MRI breast examinations. **Study Results:** The Support Vector Machine (SVM) model effectively classified all breast MRI images, except for eight slices from malignant breast images and eight slices from benign breast images, which it failed to classify correctly. The combined features of the kinetic curve, LBP, and CNN achieved the highest accuracy of 97.3%. **Conclusion:** The experimental results revealed that integrating LBP and CNN features with a large publicly available dataset could improve breast cancer detection accuracy in DCE-MRI examinations. **The defense committee was composed of:** - Asst. Prof. Dr. Mazen Kamel Hamed (Chair) - Asst. Prof. Dr. Ammar Mousa Jawad (Member) - Dr. Asaad Faleh Qasim (Member) - Prof. Dr. Hussein Saleh Hassan (Supervisor) - Asst. Prof. Dr. Ali Majid Hassan (Supervisor) The thesis was successfully accepted with a grade of distinction. 
|
