.png){kind=icon}
返回
Ensemble coupled convolution network for three-class brain tumor grade classification
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-023-17760-1.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The brain tumor grade classification is one of the prevalent tasks in brain tumor image classification. The existing models have employed transfer learning and are unable to preserve semantic features. Moreover, the results are reported on small datasets with pre-trained models. Thus, there is a need for an optimized model that can exhibit superior performance on larger datasets. We have proposed an efficientnet and coupled convolution network for the grade classification of brain magnetic resonance images. The feature extraction is performed using a pre-trained EfficientNetB0. Then, we have proposed a coupled convolution network for feature enhancement. Finally, enhanced features are classified using a fully connected dense network. We have utilized a global average pooling and dropout layers to avoid model overfitting. We have evaluated the proposed model on the REMBRANDT dataset and have achieved 96.95% accuracy. The proposed model outperforms existing pre-trained models and state-of-the-art models in vital metrics.
Keyword:
Coupled convolution network
Brain tumor grade classification
Transfer learning
Feature extraction
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
机构
引用论文
Trp-dependent auxin biosynthesis in Arabidopsis: involvement of cytochrome P450s CYP79B2 and CYP79B3
OAAI
OAAI分享收藏CGHF: A Computational Decision Support System for Glioma Classification Using Hybrid Radiomics- and Stationary Wavelet-Based Features.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Brain tumor segmentation and grading of lower-grade glioma using deep learning in MRI images基于深度学习的MRI图像低级别胶质瘤脑肿瘤分割与分级
PREAI
分享收藏Rembrandt: Helping Personalized Medicine Become a Reality through Integrative Translational Research
OAAI
OAAI分享收藏Comparison of Feature Selection Methods and Machine Learning Classifiers for Radiomics Analysis in Glioma Grading用于胶质瘤分级的放射组学分析的特征选择方法和机器学习分类器的比较IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Diagnostic Value of miR-142-5p and Tiam-1 in Colon Cancer PatientsmiR-142-5p和Tiam-1在结肠癌患者中的诊断价值
PREAI
分享收藏
查看更多内容