Lung vessel segmentation and abnormality classification based on hybrid mobile-Lenet using CT image

IF 4.9 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biomedical Signal Processing and Control Pub Date : 2024-10-24 DOI:10.1016/j.bspc.2024.107072

Sadish Sendil Murugaraj , Kalpana Vadivelu , Prabhu Thirugnana Sambandam , B. Santhosh Kumar

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引用次数: 0

Abstract

It is acknowledged from studies that viral pneumonia affects the lung vessels. Nevertheless, the diagnostic ability of a chest Computed Tomography (CT) imaging parameter is rarely leveraged. This research introduced the Hybrid Mobile LeNet (HM-LeNet) for lung vessel segmentation and abnormality classification. Firstly, the input image of CT is obtained from the database. Later, the preprocessing procedure is executed by utilizing the Non-Local Means (NLM) filter. Then, the lung lobe segmentation is carried out by using the K-Net. After that, the pulmonary vessel segmentation is performed. Finally, the features are extracted to classify the lung abnormality by utilizing the designed HM-LeNet, which is the integration of MobileNet and LeNet. The lung abnormalities are classified as emphysema, nodules, or pulmonary embolisms. The established HM-LeNet attained the maximum accuracy, True Positive Rate (TPR), and True Negative Rate (TNR) of 92.7%, 96.6%, and 94.7% respectively.

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基于混合移动网络的肺血管分割和异常分类（使用 CT 图像

研究表明，病毒性肺炎会影响肺血管。然而，胸部计算机断层扫描（CT）成像参数的诊断能力却很少被利用。本研究引入了混合移动 LeNet（HM-LeNet）用于肺血管分割和异常分类。首先，从数据库中获取 CT 输入图像。然后，利用非局部均值（NLM）滤波器执行预处理程序。然后，利用 K-Net 进行肺叶分割。然后，进行肺血管分割。最后，利用设计的 HM-LeNet 对肺部异常进行分类，HM-LeNet 是 MobileNet 和 LeNet 的集成。肺部异常被分为肺气肿、肺结节或肺栓塞。所建立的 HM-LeNet 的最高准确率、真阳性率 (TPR) 和真阴性率 (TNR) 分别为 92.7%、96.6% 和 94.7%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Biomedical Signal Processing and Control 工程技术-工程：生物医学

CiteScore

9.80

自引率

13.70%

发文量

822

审稿时长

4 months

期刊介绍： Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management. Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.