Gökalp Tulum

Gökalp Tulum is a researcher affiliated with Arel University (Istanbul Arel University) and has contributed extensively to Medical Imaging, Machine Learning, and Biomedical Engineering. His work focuses on Computer-Aided Diagnosis (CAD) systems for detecting medical conditions like pulmonary embolism, kidney trauma, and diabetic foot complications using CT scans, MRI, and deep learning. He has collaborated with researchers including Onur Osman, Uygar Teomete, and Ferhat Cüce on projects related to organ segmentation, volume calculation, and remote patient monitoring.

• Research Interests
  • Development of automated segmentation tools for abdominal organs (e.g., spleen, kidney, renal cortex/medulla).
  • Application of radiomics and deep learning in differential diagnosis of neurological and diabetic conditions.
  • Validation of CAD systems for trauma detection and volume measurement accuracy via phantom studies.
• Key Publications
  • 2025: AI-driven methods for central nervous system infection and trigeminal nerve classification.
  • 2024: Image processing techniques for CNC tool breakage detection and pulmonary embolism diagnosis.
  • 2023: Comparative analysis of radiomics and deep learning in small datasets for cancer metastasis differentiation.
• Technical Contributions
  • Co-developer of ManSeg 2.6b, a semi-automated segmentation tool for abdominal organs.
  • Proposed Butterworth-based volume calculation algorithms for kidneys and renal substructures.
  • Created projection image-based classification systems for colonic polyps using convolutional neural networks.