Ari, Berna GurlerUzen, HuseyinSengur, Abdulkadir2026-04-042026-04-042024979-8-3503-8897-8979-8-3503-8896-12165-0608https://doi.org/10.1109/SIU61531.2024.10600920https://hdl.handle.net/11616/10912732nd IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2024 -- Tarsus Univ Campus, Mersin, TURKEYThis study explores the utilization of the Pyramid Scene Parsing Network (PSPNet) architecture to achieve accurate segmentation of brain tumors in magnetic resonance (MR) images. Experimental evaluations were conducted on different pre-trained backbone network models, including Vgg16, Inceptionv3, Mobilenetv2, Efficientnetb0, Resnet18, Resnet34, Resnet50, Resnet101, Resnext50, and Resnext101, assessing the performance of each model in brain tumor segmentation. The results highlight the VGG16-PSPNet model as the most successful, showcasing high F1-score, mIoU, precision, recall, and accuracy values.trinfo:eu-repo/semantics/closedAccessBrain tumorsMagnetic Resonance Imaging (MRI)SegmentationPyramid Scene Parsing Network (PSPNet)Medical Image AnalysisConference Object10.1109/SIU61531.2024.106009202-s2.0-85200906203N/AWOS:001297894700161N/A