چکیده مقاله
Brain tumor segmentation focuses on differentiating between healthy and tumorous tissues Early and accurate diagnosis significantly improves the survival rate for individuals affected by this condition However, manual segmentation of brain tumors in three dimensional Magnetic Resonance Imaging MRI volumes is a labor intensive and time consuming process, with accuracy heavily reliant on the operator's expertise Therefore, there is a strong demand for an accurate and fully automated method to segment brain tumors and measure tumor size The development and enhancement of Computer Aided Diagnosis CAD systems for this purpose can greatly assist specialists In this project, we leveraged the power of deep learning networks to address tumor segmentation in brain MRI images Specifically, we employed the U Net architecture, which consists of both an encoder and a decoder This project explores how various training parameters influence the network's segmentation accuracy in a two dimensional context We conducted six distinct experiments, each with different parameter settings, and compared their results to evaluate performance
کلیدواژهها
نویسندگان
Neda Nosrati
MSC, Computer Engineering Department, Faculty of Engineering, Islamic Azad university of mashhad, Mashhad, Iran
Shiva sanati
Ph D, Computer Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
شیوه ارجاع
Nosrati، Neda و sanati، Shiva،1403،Optimizing U-Net Architecture for Brain Tumor Segmentation in MRI: A Comparative Study of Training Parameter Variations،بیست و سومین کنفرانس ملی علوم ومهندسی کامپیوتر و فناوری اطلاعات،بابل
ارائهشده در
مجموعه مقالات بیست و سومین کنفرانس ملی علوم ومهندسی کامپیوتر و فناوری اطلاعات31 خرداد 1403 · بابل