An approach for classification of lung nodules

Tumor discovery Pub Date : 2023-03-08 DOI:10.36922/td.317
Naveen Hm, N. C, M. Vn
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引用次数: 0

Abstract

The main objective of the proposed work is to develop an automated computer-aided detection (CAD) system to classify lung nodules using various classifiers from computed tomography (CT) images. One of the most important steps in lung nodule detection is the classification of nodule and non-nodule patterns in CT. The early detection of the condition helps lower the mortality rate. The developed CAD systems consist of segmentation, feature extraction, and classification. In this work, a filter method is used to segment the infected region. Later, we extracted features through and fed into classifiers such as Decision Stump (DS), Random Forest (RF), and Back Propagation Neural Network (BPNN). The experimentation was conducted on LIDC-IDRI dataset, and the results with BPNN outperformed those with DS and RF classifiers.
肺结节的一种分类方法
提出的工作的主要目的是开发一个自动计算机辅助检测(CAD)系统,使用计算机断层扫描(CT)图像中的各种分类器对肺结节进行分类。肺结节的CT诊断中最重要的步骤之一是结节和非结节的分类。这种疾病的早期发现有助于降低死亡率。所开发的CAD系统包括分割、特征提取和分类。在这项工作中,使用过滤方法对感染区域进行分割。随后,我们通过决策树桩(DS)、随机森林(RF)和反向传播神经网络(BPNN)等分类器提取特征并将其输入分类器。在LIDC-IDRI数据集上进行了实验,BPNN分类器的分类效果优于DS和RF分类器。
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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