利用GAN增强小型医疗数据集分类性能

IF 3.4 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Mohammad Alauthman, Ahmad Al-qerem, Bilal I. Sowan, A. Alsarhan, Mohammed Eshtay, A. Aldweesh, N. Aslam
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引用次数: 3

摘要

由于数据集有限,在医学领域开发有效的分类模型具有挑战性。为了解决这个问题,本研究提出使用生成对抗网络(GAN)作为数据增强技术。本研究旨在通过生成接近真实数据的合成数据来提高分类器的泛化性能、稳定性和精度。我们对13个基准医疗数据集采用特征选择和5种分类算法,并使用最小二乘GAN (LS-GAN)进行增强。使用不同比例的增强数据对生成的样本进行评估,结果表明支持向量机模型在更大样本下优于其他方法。提出的使用GAN的数据增强方法为提高医疗保健领域分类模型的性能提供了一个有前途的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing Small Medical Dataset Classification Performance Using GAN
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.
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来源期刊
Informatics
Informatics Social Sciences-Communication
CiteScore
6.60
自引率
6.50%
发文量
88
审稿时长
6 weeks
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