Convolutional Neural Networks for MRI-Based Brain Tumor Segmentation: A Comparative Analysis of State-of-the-Art Segmentation Networks

Authors : Ahmet Furkan BAYRAM, Caglar GURKAN, Abdulkadir BUDAK, Hakan KARATAŞ

Pages : 61-66

Doi:10.34110/forecasting.1190289

View : 12 | Download : 5

Publication Date : 2022-12-31

Article Type : Research Paper

Abstract :The prevalence of brain tumor is quite high. Brain tumor causes critical diseases. Also, brain tumor causes a variety of symptoms in most people. This study aims to segmentation of the tumor in the brain. For this purpose, state-of-art architectures, such as UNet, Attention UNet, Residual UNet, Attention Residual UNet, Residual UNet++, Inception UNet, LinkNet, and SegNet were used for segmentation. 592 magnetic resonance insert ignore into journalissuearticles values(MR); images were utilized in the training and testing of segmentation architectures. In the comparative analysis, Attention UNet achieved the best predictive performance with a 0.886 dice score, 0.795 IoU score, 0.881 sensitivity, 0.993 specificity, 0.891 precision, and 0.986 accuracy.
Keywords : Brain, Tumor, Segmentation, Artificial Intelligence, Deep Learning