.png){kind=icon}
返回
Combined Oriented Data Augmentation Method for Brain MRI Images
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2025.3526684.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In recent years, deep learning's use in medical imaging has grown exponentially. However, one of the biggest problems with training deep learning models is the unavailability of large amounts of data, which leads to overfitting. Collecting large quantities of labelled medical images is expensive, time-consuming, and depends on specialists' availability. In this paper, we proposed a novel method namely Oriented Combination MRI (OCMRI) for augmenting brain MRI dataset. The proposed method helps CNN models overcome overfitting and address class imbalance issues by combining Brain MRI images to generate new images. The image fusion is performed by selecting two images of the same tumor class if the Mean Squared Error (MSE) between these two images is greater than threshold 1 and lower than threshold 2. Both thresholds are adjustable, initially set by the user and automatically fine-tuned by the algorithm to control the number of images produced for each class, thus helping to address the data imbalance problem. The proposed approach was evaluated by training and testing the PRCnet model on four publicly available datasets before and after applying the proposed method to the datasets. Where the classification accuracy without data augmentation was 85.19% for dataset A, 90.12% for dataset B, 94.77% for dataset C, and 90% for dataset D respectively. After adding the synthetic data; the accuracy improved to 92.7% for dataset A, 95.37% for dataset B, 96.51% for dataset C and 98% for dataset D respectively.
Keyword:
Deep learning
Training
Accuracy
Magnetic resonance imaging
Brain modeling
Data augmentation
Data models
Overfitting
Biomedical imaging
Tumors
brain tumor
medical imaging
deep learning
MRI
brain tumor classification
convolutional neural network
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Detection of pseudo brain tumors via stacked LSTM neural networks using MR spectroscopy signals
PREAI
分享收藏A classification of MRI brain tumor based on two stage feature level ensemble of deep CNN models基于深度CNN模型的两阶段特征级集成的MRI脑肿瘤分类
PREAI
分享收藏分享收藏Role of interfacial phenomena in the behavior of alumina-supported palladium crystallites in oxygen
PREAI
分享收藏Uma abordagem robusta de cálculo aplicada a um sistema potenciométrico de medida de concentrações hidrogeniônicas em soluções aquosas.png)
Química Nova
OAAI
Química NovaIF0
OAAI分享收藏Studies on some new and rare Reported Marine Planktonic Ciliates (Ciliophora: Oligotrichia) from Coastal Waters in North China关于在中国北方沿海水域发现的一些新种和稀有报告海洋浮游纤毛虫（Ciliophora: Oligotrichia）的研究
PREAI
分享收藏
查看更多内容