优化融合深度学习模型,从 CT 图像中检测肾结石

IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
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引用次数: 0

摘要

准确诊断肾脏疾病至关重要,因为肾脏疾病是一个重大的健康问题,需要精确识别才能进行有效和适当的治疗。深度学习方法越来越被认为是生物医学领域疾病诊断的重要工具。然而,目前利用深度网络的模型经常会遇到过拟合和准确率低的挑战,需要进一步改进才能获得最佳性能。为了克服这些挑战,本文提出了两个针对 CT 图像中肾结石检测的集合模型。第一个模型名为 StackedEnsembleNet,是一个两级深度堆栈集合模型,有效整合了四个基础模型的预测结果:InceptionV3、InceptionResNetV2、MobileNet 和 Xception。通过利用这些模型的集体知识,StackedEnsembleNet 提高了肾结石检测的准确性和可靠性。第二个模型 PSOWeightedAvgNet 利用粒子群优化(PSO)算法来确定加权平均集合的最佳权重。通过 PSO,这种集合方法可在集合过程中为每个模型分配优化的权重,通过优化组合这些模型的预测结果来有效提高性能。在一个包含 1799 张 CT 图像的大型数据集上进行的实验结果表明,StackedEnsembleNet 和 PSOWeightedAvgNet 的性能均优于单个基础模型,在肾结石检测中达到了很高的准确率。此外,在未见过的数据集上进行的其他实验也验证了模型的泛化能力。与以往方法的比较证实了所提出的集合模型的卓越性能。论文还介绍了 Grad-CAM 可视化和错误案例分析,以便深入了解模型的决策过程。通过克服现有深度学习模型的局限性,StackedEnsembleNet 和 PSOWeightedAvgNet 为准确检测肾结石提供了一种前景广阔的方法,有助于改善肾脏病学领域的诊断和治疗效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An optimized fusion of deep learning models for kidney stone detection from CT images

Accurate diagnosis of kidney disease is crucial, as it is a significant health concern that demands precise identification for effective and appropriate treatment. Deep learning methods are increasingly recognized as valuable tools for disease diagnosis in the biomedical field. However, current models utilizing deep networks often encounter challenges of overfitting and low accuracy, necessitating further refinement for optimal performance. To overcome these challenges, this paper proposes the introduction of two ensemble models designed for kidney stone detection in CT images. The first model, called StackedEnsembleNet, is a two-level deep stack ensemble model that effectively integrates the predictions from four base models: InceptionV3, InceptionResNetV2, MobileNet, and Xception. By leveraging the collective knowledge of these models, StackedEnsembleNet improves the accuracy and reliability of kidney stone detection. The second model PSOWeightedAvgNet, leverages the Particle Swarm Optimization (PSO) algorithm to determine the optimal weights for the weighted average ensemble. Through PSO, this ensemble approach assigns optimized weights to each model during the ensembling process, effectively enhancing the performance by optimizing the combination of their predictions. Experimental results conducted on a large dataset of 1799 CT images demonstrate that both StackedEnsembleNet and PSOWeightedAvgNet outperform the individual base models, achieving high accuracy rates in kidney stone detection. Furthermore, additional experiments on an unseen dataset validate the models’ ability to generalize. The comparison with previous methods confirms the superior performance of the proposed ensemble models. The paper also presents Grad-CAM visualizations and error case analysis to provide insights into the decision-making processes of the models. By overcoming the limitations of existing deep learning models, StackedEnsembleNet and PSOWeightedAvgNet offer a promising approach for accurate kidney stone detection, contributing to improved diagnosis and treatment outcomes in the field of nephrology.

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来源期刊
CiteScore
10.50
自引率
8.70%
发文量
656
审稿时长
29 days
期刊介绍: In 2022 the Journal of King Saud University - Computer and Information Sciences will become an author paid open access journal. Authors who submit their manuscript after October 31st 2021 will be asked to pay an Article Processing Charge (APC) after acceptance of their paper to make their work immediately, permanently, and freely accessible to all. The Journal of King Saud University Computer and Information Sciences is a refereed, international journal that covers all aspects of both foundations of computer and its practical applications.
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