A Novel Automatic Lung Nodule Classification Scheme using Fusion Ghost Convolution and Hybrid Normalization in Chest CTs.

IF 1.1 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Yu Gu, Nan Wang, Jiaqi Liu, Lidong Yang, Baohua Zhang, Jing Wang, Xiaoqi Lu, Jianjun Li, XIn Liu, Siyuan Tang, Qun He
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引用次数: 0

Abstract

Objective: To address the low efficiency of diagnosing pulmonary nodules using computed tomography (CT) images and the difficulty in obtaining the key signs of malignant pulmonary nodules, a ghost convolution residual network incorporating hybrid normalization (GCHN-net) is proposed.

Methods: Firstly, a three-dimensional ghost convolution with a small kernel is embedded in the GCHN-net. Secondly, we designed a hybrid normalizedactivation module (TMNAM) that can handle the rich and complex features of lung nodules in both the deep and shallow layers of the network, and incorporating two different normalization methods. This allows the network to comprehensively learn the intricate relationships underlying the intrinsic features of lung nodules and enhances its capacity to classify the properties of unknown nodules. Additionally, to enhance the accuracy and detail of the category activation map, GradCAM++ is integrated into the third layer of the GCHN-net. This integration enables the visualization of specific regions within three-dimensional lung nodules that the model focuses on during its predictions.

Results: The accuracy of the GCHN-net on the Lung Nodule Analysis 16 (LUNA16) dataset was 90.22%, with an F1-score of 88.31% and a G-mean of 90.48%.

Conclusion: Compared with existing methods, the proposed method can greatly improve the classification of pulmonary nodules and can effectively assist doctors in diagnosing patients with pulmonary nodules.

一种基于融合鬼卷积和混合归一化的胸部ct肺结节自动分类方法。
目的:针对CT图像诊断肺结节效率低、恶性肺结节关键征象难以获取的问题,提出一种混合归一化的鬼卷积残差网络(GCHN-net)。方法:首先,在GCHN-net中嵌入具有小核的三维鬼卷积;其次,我们设计了一个混合归一化激活模块(TMNAM),该模块可以处理肺结节在网络的深层和浅层的丰富和复杂的特征,并结合了两种不同的归一化方法。这使得网络能够全面了解肺结节内在特征背后的复杂关系,并增强其对未知结节属性的分类能力。此外,为了提高分类激活图的准确性和细节性,我们将GradCAM++集成到gcn -net的第三层。这种集成使模型在预测过程中关注的三维肺结节内的特定区域可视化。结果:GCHN-net在肺结节分析16 (LUNA16)数据集上的准确率为90.22%,f1评分为88.31%,g均值为90.48%。结论:与现有方法相比,所提出的方法可以大大提高肺结节的分类,有效地辅助医生对肺结节患者的诊断。
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来源期刊
CiteScore
2.60
自引率
0.00%
发文量
246
审稿时长
1 months
期刊介绍: Current Medical Imaging Reviews publishes frontier review articles, original research articles, drug clinical trial studies and guest edited thematic issues on all the latest advances on medical imaging dedicated to clinical research. All relevant areas are covered by the journal, including advances in the diagnosis, instrumentation and therapeutic applications related to all modern medical imaging techniques. The journal is essential reading for all clinicians and researchers involved in medical imaging and diagnosis.
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