Accurate Segmentation of Brain Tumors in Magnetic Resonance Images with Pyramid Stage Decomposition Network Approach - Ari, Berna Gurler; Uzen, Huseyin; Sengur, Abdulkadir (Ieee, 2024)
This 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.
Piramit Sahne Ayrıştırma Ağı Yaklaşımı ile Manyetik Rezonans Görüntülerinde Beyin Tümörlerinin Doğru Segmentasyonu
32nd IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2024 -- Tarsus Univ Campus, Mersin, TURKEY
Keywords: Brain tumors; Magnetic Resonance Imaging (MRI); Segmentation; Pyramid Scene Parsing Network (PSPNet); Medical Image Analysis