Analysis of Machine Learning Algorithms for Diagnosis of Diffuse Lung Diseases

• Published in: Methods of information in medicine Vol. 57; no. 5-06; p. 272
• Main Authors: Cardoso, Isadora, Almeida, Eliana, Allende-Cid, Hector, Frery, Alejandro C, Rangayyan, Rangaraj M, Azevedo-Marques, Paulo M, Ramos, Heitor S
• Format: Journal Article
• Language: English
• Published: Germany 01.11.2018
• Subjects: Algorithms; Diagnosis, Computer-Assisted; Discriminant Analysis; Humans; Lung Diseases - diagnosis; Machine Learning; Principal Component Analysis; Time Factors
• ISSN: 2511-705X
• DOI: 10.1055/s-0039-1681086

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.