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Analysis of Machine Learning Algorithms for Diagnosis of Diffuse Lung Diseases.
Methods of Information in Medicine ( IF 1.7 ) Pub Date : 2018-11-01 , DOI: 10.1055/s-0039-1681086
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  

Computational Intelligence Re-meets Medical Image Processing A Comparison of Some Nature-Inspired Optimization Metaheuristics Applied in Biomedical Image Registration 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:

诊断弥漫性肺部疾病的机器学习算法分析。

计算智能重新满足医学图像处理的一些自然启发式优化元启发式方法在生物医学图像配准中的应用背景:弥漫性肺部疾病(DLD)是多种肺部疾病,其特征是肺组织发炎,可能导致永久性炎症丧失呼吸和死亡的能力。由于疾病种类繁多且原因不明,因此在这些疾病之间进行区分对医师而言是一项挑战。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提取28个特征。我们使用主成分分析,线性判别分析和逐步选择-前向,后向和前向后向来减少特征维。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。
更新日期:2018-11-01
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