Özgür Dandinمشاهده پروفایل
پژوهشگر
Özgür Dandin is a researcher at Arel University specializing in medical image processing and computer-aided diagnosis systems. His work primarily focuses on developing automated segmentation algorithms for abdominal organs, particularly in trauma cases, with publications spanning from 2015 to 2020. His research interests include: Medical image segmentation and analysis Computer-aided detection systems for trauma diagnosis Organ volume calculation methods Application of deep learning in medical imaging Development of software tools like ManSeg 2.6b for radiological assessment Dandin's publication record demonstrates consistent focus on improving diagnostic accuracy through computational methods across various abdominal organs including kidneys, liver, spleen, and intestines. His work bridges engineering and clinical applications, collaborating with medical professionals to ensure clinical relevance. Key contributions include automated segmentation methods for injured organs, novel approaches for hematoma volume calculation, validation studies of medical image analysis tools, and applications of convolutional neural networks in polyp classification. His scientific collaborations are primarily with Gökalp Tulum, Onur Osman, and Tuncer Ergin, resulting in numerous publications in IEEE conferences and journals. The consistent publication record from 2015 through 2020 indicates active engagement in the field of medical image analysis with practical applications in emergency medicine, radiology, and surgical specialties.
