Cardoso Isadora, Almeida Eliana, Allende-Cid Hector, Frery Alejandro C, Rangayyan Rangaraj M, Azevedo-Marques Paulo M, Ramos Heitor S
Instituto de Computação, Universidade Federal de Alagoas, Maceió, Brazil.
Escuela de Ingeniería Informatica, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile.
Methods Inf Med. 2018 Nov;57(5-06):272-279. doi: 10.1055/s-0039-1681086. Epub 2019 Mar 15.
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.
Exploring the potential of dimensionality reduction combined with ML methods for diagnosis of DLDs; improving the classification accuracy over state-of-the-art 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.
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.
This work contributes to the analysis and selection of features that can efficiently characterize the DLDs studied.
计算智能与医学图像处理的再相遇——一些应用于生物医学图像配准的受自然启发的优化元启发式算法的比较
弥漫性肺疾病（DLD）是一组多样的肺部疾病，其特征为肺组织炎症，这可能导致呼吸能力的永久性丧失甚至死亡。由于这些疾病种类繁多且病因不明，医生区分它们具有挑战性。计算机辅助诊断（CAD）是一种通过将专家提供的信息与机器学习（ML）方法相结合来提高诊断准确性的有用方法。
探索降维与ML方法相结合用于DLD诊断的潜力；比现有方法提高分类准确率。
使用了一个由3252个感兴趣区域（ROI）组成的数据集，每个ROI提取28个特征。我们使用主成分分析、线性判别分析以及逐步选择法（向前、向后和向前向后）来降低特征维度。获得的特征子集被用作以下ML方法的输入：支持向量机、高斯混合模型、k近邻和深度前馈神经网络。我们还将深度卷积神经网络直接应用于ROI。
使用LDA我们实现了从28维到5维的最大降维。DFNN获得了最佳分类结果，总体准确率为99.60%。
这项工作有助于对能够有效表征所研究的DLD的特征进行分析和选择。
