The student Ruqayyah Ahmed Muhammad Ali Abbas Sharaf Al-Din obtained a master’s degree with a score of 95% and an “excellent” grade in the Department of (Computer Science)
The student Ruqayyah Ahmed Muhammad Ali Abbas Sharaf Al-Din obtained a master’s degree with a score of 95% and an “excellent” grade in the Department of (Computer Science), specializing in (Computer Science), College of (Computer and Information Technology) at Sana’a University, for her thesis entitled (“An Improvement Mitosis Detection in Breast Cancer Histopathology Images by Using Yolov5”) on Tuesday, Hijri date 04/03/1446 corresponding to 07/09/2024 AD.
The discussion and judging committee consisted of Associate Professor (Musa Muslih Ahmed Ghorab), the main supervisor and member of the committee, Professor Dr. (Fouad Hassan Muhammad Abdul Razzaq), an external examiner and head of the committee, and Dr. (Ahmed Abdullah Al-Shalabi), an internal examiner and member of the committee.
The message aimed to detect the number of cancer cells, one of the necessary procedures that must be performed in the diagnosis of breast cancer because it is an important indicator to determine the aggressiveness of the tumor (i.e. whether there is a possibility of these cancer cells attacking other healthy organs in the body). Deep learning algorithms have many contributions in medical fields, including the field of mitosis detection, as the task of mitosis detection is a difficult and tiring task that requires time and effort from pathologists (diagnostic doctors) because the working environment under high magnification microscopes. To ease this burden on pathologists and speed up patient diagnosis, deep learning techniques have been increasingly used. For this reason, several international competitions have been held starting from 2012, where the mitosis detection challenge was held at the International Conference on Model Recognition by Providing DataSet (ICPR 2012). In 2013, the Miccai Grand Challenge (to evaluate mitosis detection algorithms) was held and a DataSet (AMIDA13) was provided for the mitosis detection challenge. In 2014, a grand challenge was held where researchers were asked to compete on the result of Nuclear Neglect and Mitotic Reductions DataSet (ICPR 2014). The discussion was attended by a number of faculty members, students, interested parties, and a number of the student’s colleagues and family members.
