Accurate Segmentation of Brain Tumors in Magnetic Resonance Images with Pyramid Stage Decomposition Network Approach

被引：0

作者：

Ari, Berna Gurler ^{[1
]}

Uzen, Huseyin ^{[2
]}

Sengur, Abdulkadir ^{[3
]}

机构：

[1] Inonu Univ, Bilgisayar Muhendisligi Bolumu, Malatya, Turkiye

[2] Bingo Univ, Bilgisayar Muhendisligi Bolumu, Bingo, Turkiye

[3] Firat Univ, Elekt Elekt Teknol Bolumu, Elazig, Turkiye

来源：

32ND IEEE SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE, SIU 2024 | 2024年

关键词：

Brain tumors; Magnetic Resonance Imaging (MRI); Segmentation; Pyramid Scene Parsing Network (PSPNet); Medical Image Analysis;

D O I：

10.1109/SIU61531.2024.10600920

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页数：4

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