使用基于蜣螂优化的 LNS-DualMAGNet 模型增强肺结节分段能力

Sathyamoorthy K, Ravikumar S
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引用次数: 0

摘要

这项研究的重点是肺结节,因为肺结节经常与肺癌有关,而肺癌是世界上最常见的癌症致死原因。在临床实践中,及时、准确地诊断这些结节至关重要,尽管这很困难。为了进行诊断,该研究使用了肺部图像数据库联盟和 LIDC-IDRI 数据集的 CT 扫描图像。预处理包括使用高斯平滑(GS)滤波器降噪和对比度增强。通过双路径多尺度注意融合模块(DualMAF)和多尺度归一化通道注意模块(MNCA),该研究提出了用于肺结节分割的 LNS-DualMAGNet 模型。这些模块利用深度可分离卷积和注意力机制等新方法,提高了各通道之间的相互依存性和语义理解能力。为了提高性能,该模型还加入了 DSConv 和 Resnet34 模块。Dung Beetle 优化算法(DBOA)用于调整所提议的分类器的超参数。研究结果表明,所提出的模型比现有的方法表现更好,准确率和 DSC 均达到 0.99,这表明该模型具有提高临床诊断肺结节分割的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhanced Lung Nodule Segmentation using Dung Beetle Optimization based LNS-DualMAGNet Model
The study's focus is on lung nodules, which are frequently connected to lung cancer, the world's most common cause of cancer-related deaths. In clinical practice, a timely and precise diagnosis of these nodules is essential, albeit difficult. For diagnosis, the study used CT scans from the Lung Image Database Consortium and the LIDC-IDRI dataset. Noise reduction with a Gaussian Smoothing (GS) Filter and contrast enhancement were part of the preprocessing. With a Dual-path Multi-scale Attention Fusion Module (DualMAF) and a Multi-scale Normalized Channel Attention Module (MNCA), the study presented the LNS-DualMAGNet model for lung nodule segmentation. These modules improve interdependence across channels and semantic understanding by utilizing novel approaches such as Depthwise Separable Convolutions and attention mechanisms. For increased performance, the model also incorporates DSConv and a Resnet34 block. The Dung Beetle Optimization Algorithm (DBOA) was used for tuning the hyperparameter of the proposed classifier. Findings indicated that the proposed model performed better than the existing approaches, attaining a 0.99 accuracy and DSC, indicating its potential to enhance lung nodule segmentation for clinical diagnosis.
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