Convolutional Neural Networks for MRI-Based Brain Tumor Segmentation: A Comparative Analysis of State-of-the-Art Segmentation Networks
Convolutional Neural Networks for MRI-Based Brain Tumor Segmentation: A Comparative Analysis of State-of-the-Art Segmentation Networks
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 (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,
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- Yayın Aralığı: Yılda 2 Sayı
- Başlangıç: 2017
- Yayıncı: Giresun Üniversitesi
Sayıdaki Diğer Makaleler
Ali ÖZDEMİR, Aytuğ ONAN, Vildan ÇINARLI ERGENE
Ahmet Furkan BAYRAM, Caglar GURKAN, Abdulkadir BUDAK, Hakan KARATAŞ
Ahmet Furkan BAYRAM, Caglar GURKAN, Abdulkadir BUDAK, Hakan KARATAŞ