Automatic Feature Selection for Stenosis Detection in X-ray Coronary Angiograms

Miguel-Angel Gil-Rios, Igor V. Guryev, Ivan Cruz-Aceves, Juan Gabriel Avina-Cervantes, Martha Alicia Hernandez-Gonzalez, Sergio Eduardo Solorio-Meza and Juan Manuel Lopez-Hernandez
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Miguel-Angel Gil-Rios: División de Ingenierías (DICIS), Campus Irapuato-Salamanca, Universidad de Guanajuato, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 km, Comunidad de Palo Blanco, Salamanca 36885, Guanajuato, Mexico
Igor V. Guryev: División de Ingenierías (DICIS), Campus Irapuato-Salamanca, Universidad de Guanajuato, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 km, Comunidad de Palo Blanco, Salamanca 36885, Guanajuato, Mexico
Ivan Cruz-Aceves: CONACYT-Center for Research in Mathematics (CIMAT), Valenciana 36023, Guanajuato, Mexico
Juan Gabriel Avina-Cervantes: División de Ingenierías (DICIS), Campus Irapuato-Salamanca, Universidad de Guanajuato, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 km, Comunidad de Palo Blanco, Salamanca 36885, Guanajuato, Mexico
Martha Alicia Hernandez-Gonzalez: Unidad Médica de Alta Especialidad (UMAE)-Hospital de Especialidades No.1. Centro Médico Nacional del Bajio, León 37320, Guanajuato, Mexico
Sergio Eduardo Solorio-Meza: Department of Health Sciences, Universidad Tecnológica de México (UNITEC) Campus León, León 37200, Gto., Mexico
Juan Manuel Lopez-Hernandez: División de Ingenierías (DICIS), Campus Irapuato-Salamanca, Universidad de Guanajuato, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 km, Comunidad de Palo Blanco, Salamanca 36885, Guanajuato, Mexico

Mathematics, 2021, vol. 9, issue 19, 1-18

Abstract: The automatic detection of coronary stenosis is a very important task in computer aided diagnosis systems in the cardiology area. The main contribution of this paper is the identification of a suitable subset of 20 features that allows for the classification of stenosis cases in X-ray coronary images with a high performance overcoming different state-of-the-art classification techniques including deep learning strategies. The automatic feature selection stage was driven by the Univariate Marginal Distribution Algorithm and carried out by statistical comparison between five metaheuristics in order to explore the search space, which is O ( 2 49 ) computational complexity. Moreover, the proposed method is compared with six state-of-the-art classification methods, probing its effectiveness in terms of the Accuracy and Jaccard Index evaluation metrics. All the experiments were performed using two X-ray image databases of coronary angiograms. The first database contains 500 instances and the second one 250 images. In the experimental results, the proposed method achieved an Accuracy rate of 0.89 and 0.88 and Jaccard Index of 0.80 and 0.79 , respectively. Finally, the average computational time of the proposed method to classify stenosis cases was ?0.02 s, which made it highly suitable to be used in clinical practice.

Keywords: coronary stenosis; feature selection; support vector machines; Univariate Marginal Distribution Algorithm; X-ray imaging (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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