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About Brain Tumor Segmentation and Detection Using Deep Learning
This document summarizes several papers on brain tumor segmentation and detection using deep learning techniques. It begins with an introduction on brain tumors and the need for automatic detection methods. It then summarizes two key papers: 1) A paper by Dong et al. that proposes a fully convolutional network based on the U-Net architecture for brain tumor segmentation. It achieved Dice scores of 0.86, 0.86 and 0.65 for whole, core and enhancing tumors respectively on BRATS data. 2) A paper presenting a technique using CNNs that sorts MRI images by tumor type (meningioma, glioma, pituitary) and trains separate LinkNet models on each group, achieving accurate segmentation results.
Detailed Information
| Author: | ['mohammad arshad siddique'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 5 |
| Language: | English |
| Format: | |
| Price: | FREE |
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