一种基于数据增强的心肌图像分类新方法

IF 0.7 Q3 COMPUTER SCIENCE, THEORY & METHODS
Qingyong Zhu
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引用次数: 0

摘要

心肌炎是一个重要的公共卫生问题,因为它可以导致心力衰竭和猝死。它可以通过心脏磁共振成像(MRI)进行诊断,这是一种无创成像技术,但可能存在操作员偏见。该研究为使用CMR图像检测心肌炎提供了一个基于深度学习的模型,以支持医疗专业人员。所提出的架构包括卷积神经网络(CNN)、全连接决策层、基于生成对抗网络(GAN)的数据增强算法、用于预训练权重的增强DE和基于强化学习的训练方法。我们提出了一种基于GAN的利用生成图像进行数据增强的新方法,以提高所提供CNN的分类性能。不平衡数据是最重要的分类问题之一,因为负样本多于正样本,从而降低系统性能。为了解决这一问题,我们提出了一种基于rl的训练方法,即集中学习少数类样本。此外,我们还解决了与训练步骤相关的挑战,该步骤通常依赖于基于梯度的学习过程技术;然而,这些方法经常面临诸如初始化敏感性之类的问题。为了启动BP过程,我们提出了一种改进的差分进化(DE)技术,该技术利用了基于聚类的突变算子。它为DE识别成功的集群,并应用原始的更新策略来生成潜在的解决方案。我们在Z-Alizadeh Sani心肌炎数据集上评估了我们建议的模型,并表明它优于其他方法。Keywords-Myocarditis;生成对抗网络;数据增加;微分进化
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Method for Myocardial Image Classification using Data Augmentation
Myocarditis is an important public health concern since it can cause heart failure and abrupt death. It can be diagnosed with magnetic resonance imaging (MRI) of the heart, a non-invasive imaging technology with the potential for operator bias. The study provides a deep learning-based model for myocarditis detection using CMR images to support medical professionals. The proposed architecture comprises a convolutional neural network (CNN), a fully-connected decision layer, a generative adversarial network (GAN)-based algorithm for data augmentation, an enhanced DE for pre-training weights, and a reinforcement learning-based method for training. We present a new method of employing produced images for data augmentation based on GAN to improve the classification performance of the provided CNN. Unbalanced data is one of the most significant classification issues, as negative samples are more than positive, decimating system performance. To solve this issue, we offer an RL-based training method that learns minority class examples with attention. In addition, we tackle the challenges associated with the training step, which typically relies on gradient-based techniques for the learning process; however, these methods often face issues like sensitivity to initialization. To start the BP process, we present an improved differential evolution (DE) technique that leverages a clustering-based mutation operator. It recognizes a successful cluster for DE and applies an original updating strategy to produce potential solutions. We assess our suggested model on the Z-Alizadeh Sani myocarditis dataset and show that it outperforms other methods. Keywords—Myocarditis; generative adversarial network; data augmentation; differential evolution
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来源期刊
CiteScore
2.30
自引率
22.20%
发文量
519
期刊介绍: IJACSA is a scholarly computer science journal representing the best in research. Its mission is to provide an outlet for quality research to be publicised and published to a global audience. The journal aims to publish papers selected through rigorous double-blind peer review to ensure originality, timeliness, relevance, and readability. In sync with the Journal''s vision "to be a respected publication that publishes peer reviewed research articles, as well as review and survey papers contributed by International community of Authors", we have drawn reviewers and editors from Institutions and Universities across the globe. A double blind peer review process is conducted to ensure that we retain high standards. At IJACSA, we stand strong because we know that global challenges make way for new innovations, new ways and new talent. International Journal of Advanced Computer Science and Applications publishes carefully refereed research, review and survey papers which offer a significant contribution to the computer science literature, and which are of interest to a wide audience. Coverage extends to all main-stream branches of computer science and related applications
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