.png){kind=icon}
返回
Multi-class brain tumor classification using residual network and global average pooling
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-020-10335-4.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A rapid increase in brain tumor cases mandates researchers for the automation of brain tumor detection and diagnosis. Multi-tumor brain image classification became a contemporary research task due to the diverse characteristics of tumors. Recently, deep neural networks are commonly used for medical image classification to assist neurologists. Vanishing gradient problem and overfitting are the demerits of the deep networks. In this paper, we have proposed a deep network model that uses ResNet-50 and global average pooling to resolve the vanishing gradient and overfitting problems. To evaluate the efficiency of the proposed model simulation has been carried out using a three-tumor brain magnetic resonance image dataset consisting of 3064 images. Key performance metrics have used to analyze the performance of the proposed model and its competitive models. We have achieved a mean accuracy of 97.08% and 97.48% with data augmentation and without data augmentation, respectively. Our proposed model outperforms existing models in classification accuracy.
Keyword:
Multi-class classification
Brain tumor classification
Deep learning
Residual network
Global average pooling
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
机构
引用论文
Computer-aided diagnosis in medical imaging: Historical review, current status and future potential医学影像中的计算机辅助诊断: 历史回顾，现状和未来潜力
OAAI
.png){kind=icon}
OAAI分享收藏Brain tumor classification for MR images using transfer learning and fine-tuning使用迁移学习和微调对MR图像进行脑肿瘤分类
PREAI
分享收藏Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm通过MRI计算机辅助诊断人脑肿瘤: 一项调查和一种新算法
PREAI
分享收藏Developing Wind and/or Solar Powered Crop Irrigation Systems for the Great Plains为大平原开发风能和/或太阳能作物灌溉系统
PREAI
分享收藏
查看更多内容