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一种用于肺癌分类系统的优化支持向量机。

An optimized support vector machine for lung cancer classification system.

作者信息

Oyediran Mayowa O, Ojo Olufemi S, Raji Ibrahim A, Adeniyi Abidemi Emmanuel, Aroba Oluwasegun Julius

机构信息

Department of Computer Engineering, Ajayi Crowther University, Oyo, Nigeria.

Department of Computer Sciences, Ajayi Crowther University, Oyo, Nigeria.

出版信息

Front Oncol. 2024 Dec 23;14:1408199. doi: 10.3389/fonc.2024.1408199. eCollection 2024.

DOI:10.3389/fonc.2024.1408199
PMID:39763607
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11700792/
Abstract

INTRODUCTION

Lung cancer is one of the main causes of the rising death rate among the expanding population. For patients with lung cancer to have a higher chance of survival and fewer deaths, early categorization is essential. The goal of thisresearch is to enhance machine learning to increase the precision and quality of lung cancer classification.

METHODS

The dataset was obtained from an open-source database and was utilized for testing and training. The suggested system used a CT scan picture as its input image, and it underwent a variety of image processing operations, including segmentation, contrast enhancement, and feature extraction.

RESULTS

The training process produces a chameleon swarm-based supportvector machine that can identify between benign, malignant, and normal nodules.

CONCLUSION

The performance of the system is evaluated in terms of false-positive rate (FPR), sensitivity, specificity, recognition time and recognition accuracy.

摘要

引言

肺癌是不断增长的人口中死亡率上升的主要原因之一。对于肺癌患者来说，为了有更高的生存几率和更低的死亡率，早期分类至关重要。本研究的目标是改进机器学习，以提高肺癌分类的精度和质量。

方法

数据集从一个开源数据库获取，并用于测试和训练。所提出的系统使用CT扫描图像作为输入图像，并进行了各种图像处理操作，包括分割、对比度增强和特征提取。

结果

训练过程产生了一种基于变色龙群的支持向量机，它可以区分良性、恶性和正常结节。

结论

根据假阳性率（FPR）、灵敏度、特异性、识别时间和识别准确率对系统性能进行评估。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/280b/11700792/26e1cefe9b62/fonc-14-1408199-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/280b/11700792/da776de55176/fonc-14-1408199-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/280b/11700792/77c61141a435/fonc-14-1408199-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/280b/11700792/8a75ca29f01f/fonc-14-1408199-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/280b/11700792/21917b78330f/fonc-14-1408199-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/280b/11700792/26e1cefe9b62/fonc-14-1408199-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/280b/11700792/da776de55176/fonc-14-1408199-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/280b/11700792/77c61141a435/fonc-14-1408199-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/280b/11700792/8a75ca29f01f/fonc-14-1408199-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/280b/11700792/21917b78330f/fonc-14-1408199-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/280b/11700792/26e1cefe9b62/fonc-14-1408199-g005.jpg

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Spa-RQ: an Image Analysis Tool to Visualise and Quantify Spatial Phenotypes Applied to Non-Small Cell Lung Cancer.Spa-RQ：一种用于可视化和量化非小细胞肺癌空间表型的图像分析工具。
Sci Rep. 2019 Nov 26;9(1):17613. doi: 10.1038/s41598-019-54038-9.
2
Breast cancer detection using deep convolutional neural networks and support vector machines.使用深度卷积神经网络和支持向量机进行乳腺癌检测。
PeerJ. 2019 Jan 28;7:e6201. doi: 10.7717/peerj.6201. eCollection 2019.
3
Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization.
基于梯度提升树和贝叶斯优化的肺结节计算机辅助诊断。
PLoS One. 2018 Apr 19;13(4):e0195875. doi: 10.1371/journal.pone.0195875. eCollection 2018.
4
Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.支持向量机（SVM）学习在癌症基因组学中的应用。
Cancer Genomics Proteomics. 2018 Jan-Feb;15(1):41-51. doi: 10.21873/cgp.20063.