当前位置: X-MOL 学术Methods Inf. Med. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Analysis of Machine Learning Algorithms for Diagnosis of Diffuse Lung Diseases
Methods of Information in Medicine ( IF 1.7 ) Pub Date : 2018-10-08 , DOI: 10.3414/me17-02-0023
Isadora Cardoso 1 , Eliana Almeida 1 , Hector Allende-Cid 2 , Alejandro C Frery 1 , Rangaraj M Rangayyan 3 , Paulo M Azevedo-Marques 4 , Heitor S Ramos 1
Affiliation  

BACKGROUND Diffuse lung diseases (DLDs) are a diverse group of pulmonary disorders, characterized by inflammation of lung tissue, which may lead to permanent loss of the ability to breathe and death. Distinguishing among these diseases is challenging to physicians due their wide variety and unknown causes. Computer-aided diagnosis (CAD) is a useful approach to improve diagnostic accuracy, by combining information provided by experts with Machine Learning (ML) methods. OBJECTIVES Exploring the potential of dimensionality reduction combined with ML methods for diagnosis of DLDs; improving the classification accuracy over state-of-the-art methods. METHODS A data set composed of 3252 regions of interest (ROIs) was used, from which 28 features were extracted per ROI. We used Principal Component Analysis, Linear Discriminant Analysis, and Stepwise Selection - Forward, Backward, and Forward-Backward to reduce feature dimensionality. The feature subsets obtained were used as input to the following ML methods: Support Vector Machine, Gaussian Mixture Model, k-Nearest Neighbor, and Deep Feedforward Neural Network. We also applied a Deep Convolutional Neural Network directly to the ROIs. RESULTS We achieved the maximum reduction from 28 to 5 dimensions using LDA. The best classification results were obtained by DFNN, with 99.60% of overall accuracy. CONCLUSIONS This work contributes to the analysis and selection of features that can efficiently characterize the DLDs studied.

中文翻译:

诊断弥漫性肺疾病的机器学习算法分析

背景技术弥漫性肺部疾病(DLD)是多种肺部疾病,其特征在于肺组织发炎,这可能导致呼吸和死亡能力的永久丧失。由于疾病种类繁多且病因不明,因此如何区分这些疾病给医生们带来了挑战。通过将专家提供的信息与机器学习(ML)方法结合起来,计算机辅助诊断(CAD)是提高诊断准确性的有用方法。目的探索降维与ML方法相结合的诊断DLD的潜力;与最新方法相比,提高了分类准确性。方法使用由3252个感兴趣区域(ROI)组成的数据集,从每个ROI中提取28个特征。我们使用了主成分分析,线性判别分析,和逐步选择-向前,向后和向前-向后以减小特征维。获得的特征子集用作以下ML方法的输入:支持向量机,高斯混合模型,k最近邻和深度前馈神经网络。我们还直接将深层卷积神经网络应用于ROI。结果我们使用LDA将尺寸从28减少到5。DFNN获得了最好的分类结果,整体准确率达99.60%。结论这项工作有助于分析和选择可以有效表征所研究DLD的特征。k最近邻和深度前馈神经网络。我们还直接将深层卷积神经网络应用于ROI。结果我们使用LDA将尺寸从28减少到5。DFNN获得了最好的分类结果,整体准确率达99.60%。结论这项工作有助于分析和选择可以有效表征所研究DLD的特征。k最近邻和深度前馈神经网络。我们还直接将深层卷积神经网络应用于ROI。结果我们使用LDA将尺寸从28减少到5。DFNN获得了最好的分类结果,整体准确率达99.60%。结论这项工作有助于分析和选择可以有效表征所研究DLD的特征。
更新日期:2018-10-08
down
wechat
bug