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Transfer Learning for Stenosis Detection in X-ray Coronary Angiography

Author

Listed:
  • Emmanuel Ovalle-Magallanes

    (Telematics (CA), Engineering Division (DICIS), Campus Irapuato-Salamanca, University of Guanajuato, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 km, Comunidad de Palo Blanco, Salamanca 36885, Mexico)

  • Juan Gabriel Avina-Cervantes

    (Telematics (CA), Engineering Division (DICIS), Campus Irapuato-Salamanca, University of Guanajuato, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 km, Comunidad de Palo Blanco, Salamanca 36885, Mexico)

  • Ivan Cruz-Aceves

    (CONACYT Research-Fellow, Center for Research in Mathematics (CIMAT), A.C., Jalisco S/N, Col. Valenciana, Guanajuato 36000, Mexico)

  • Jose Ruiz-Pinales

    (Telematics (CA), Engineering Division (DICIS), Campus Irapuato-Salamanca, University of Guanajuato, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 km, Comunidad de Palo Blanco, Salamanca 36885, Mexico)

Abstract

Coronary artery disease is the most frequent type of heart disease caused by an abnormal narrowing of coronary arteries, also called stenosis or atherosclerosis. It is also the leading cause of death globally. Currently, X-ray Coronary Angiography (XCA) remains the gold-standard imaging technique for medical diagnosis of stenosis and other related conditions. This paper presents a new method for the automatic detection of coronary artery stenosis in XCA images, employing a pre-trained (VGG16, ResNet50, and Inception-v3) Convolutional Neural Network (CNN) via Transfer Learning. The method is based on a network-cut and fine-tuning approach. The optimal cut and fine-tuned layers were selected following 20 different configurations for each network. The three networks were fine-tuned using three strategies: only real data, only artificial data, and artificial with real data. The synthetic dataset consists of 10,000 images ( 80 % for training, 20 % for validation) produced by a generative model. These different configurations were analyzed and compared using a real dataset of 250 real XCA images (125 for testing and 125 for fine-tuning), regarding their randomly initiated CNNs and a fourth custom CNN, trained as well with artificial and real data. The results showed that pre-trained VGG16, ResNet50, and Inception-v3 cut on an early layer and fine-tuned, overcame the referencing CNNs performance. Specifically, Inception-v3 provided the best stenosis detection with an accuracy of 0.95 , a precision of 0.93 , sensitivity, specificity, and F 1 score of 0.98 , 0.92 , and 0.95 , respectively. Moreover, a class activation map is applied to identify the high attention regions for stenosis detection.

Suggested Citation

  • Emmanuel Ovalle-Magallanes & Juan Gabriel Avina-Cervantes & Ivan Cruz-Aceves & Jose Ruiz-Pinales, 2020. "Transfer Learning for Stenosis Detection in X-ray Coronary Angiography," Mathematics, MDPI, vol. 8(9), pages 1-20, September.
  • Handle: RePEc:gam:jmathe:v:8:y:2020:i:9:p:1510-:d:408977
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    Cited by:

    1. Rita Fabiane Teixeira Gomes & Jean Schmith & Rodrigo Marques de Figueiredo & Samuel Armbrust Freitas & Giovanna Nunes Machado & Juliana Romanini & Vinicius Coelho Carrard, 2023. "Use of Artificial Intelligence in the Classification of Elementary Oral Lesions from Clinical Images," IJERPH, MDPI, vol. 20(5), pages 1-14, February.

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