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Chapter and Conference Paper
MS UNet: Multi-scale 3D UNet for Brain Tumor Segmentation
A deep convolutional neural network (CNN) achieves remarkable performance for medical image analysis. UNet is the primary source in the performance of 3D CNN architectures for medical imaging tasks, including ...
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Chapter and Conference Paper
HI-Net: Hyperdense Inception 3D UNet for Brain Tumor Segmentation
The brain tumor segmentation task aims to classify tissue into the whole tumor (WT), tumor core (TC) and enhancing tumor (ET) classes using multimodel MRI images. Quantitative analysis of brain tumors is criti...
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Chapter and Conference Paper
Context Aware 3D UNet for Brain Tumor Segmentation
Deep convolutional neural network (CNN) achieves remarkable performance for medical image analysis. UNet is the primary source in the performance of 3D CNN architectures for medical imaging tasks, including br...
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Chapter and Conference Paper
Hybrid Labels for Brain Tumor Segmentation
The accurate automatic segmentation of brain tumors enhances the probability of survival rate. Convolutional Neural Network (CNN) is a popular automatic approach for image evaluations. CNN provides excellent r...
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Chapter and Conference Paper
Seismic Stability Analysis of Historical Construction: A Case Study - Wazirpur Tomb
The non-engineer...