基于U-Net的2020年BraTS脑肿瘤检测

2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2021-09-03 DOI:10.1109/icrito51393.2021.9596530

BharathSimhaReddy Maram, Pooja Rana

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引用次数: 1

摘要

该框架的主要目标是建立一个高效的深度学习模型来检测脑肿瘤。本文的框架主要针对MICCAI BraTS2020挑战的一部分BraTS2020数据集中的脑肿瘤MRI图像检测，采用适合快速准确图像分类的U-Net架构，训练准确率达到98.485%。与BraTs2020数据集上的其他架构相比，具有自定义的U-Net架构提供了更好的结果。

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Brain Tumour Detection on BraTS 2020 Using U-Net

Main objective of this framework is to build a efficient deep learning model to detect the brain tumor. In this paper, the framework mainly focuses on the detection of brain tumor MRI images from the BraTS2020 dataset which is a part of the MICCAI BraTS2020 challenge, using U-Net architecture which is suitable for quick and accurate image classification and achieved a training accuracy of 98.485%. When compared to other architectures on BraTs2020 dataset, U-Net architecure with customization provides better results.

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来源期刊

2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)

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