.png){kind=icon}
返回
Enhancing Small Medical Dataset Classification Performance Using GAN
.png){kind=icon}
OA
AI
DOI：10.3390/informatics10010028.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Developing an effective classification model in the medical field is challenging due to limited datasets. To address this issue, this study proposes using a generative adversarial network (GAN) as a data-augmentation technique. The research aims to enhance the classifier's generalization performance, stability, and precision through the generation of synthetic data that closely resemble real data. We employed feature selection and applied five classification algorithms to thirteen benchmark medical datasets, augmented using the least-square GAN (LS-GAN). Evaluation of the generated samples using different ratios of augmented data showed that the support vector machine model outperforms other methods with larger samples. The proposed data augmentation approach using a GAN presents a promising solution for enhancing the performance of classification models in the healthcare field.
Keyword:
data augmentation
GANs
medical dataset
machine learning
healthcare
期刊
I
IF:
2.8
论文数:
513
被引数:
1.4K
机构
引用论文
A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability基于遗传学的机器学习的统计技术和性能度量的研究: 准确性和可解释性.png)
SOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏分享收藏分享收藏
查看更多内容