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Article

Transfer Learning for Stenosis Detection in X-ray Coronary Angiography

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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
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CONACYT Research-Fellow, Center for Research in Mathematics (CIMAT), A.C., Jalisco S/N, Col. Valenciana, Guanajuato 36000, Mexico
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Author to whom correspondence should be addressed.
Mathematics 2020, 8(9), 1510; https://doi.org/10.3390/math8091510
Received: 29 July 2020 / Revised: 30 August 2020 / Accepted: 1 September 2020 / Published: 4 September 2020
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 F1 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. View Full-Text
Keywords: convolutional neural networks; coronary angiography; stenosis detection; transfer learning; X-ray imaging convolutional neural networks; coronary angiography; stenosis detection; transfer learning; X-ray imaging
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MDPI and ACS Style

Ovalle-Magallanes, E.; Avina-Cervantes, J.G.; Cruz-Aceves, I.; Ruiz-Pinales, J. Transfer Learning for Stenosis Detection in X-ray Coronary Angiography. Mathematics 2020, 8, 1510. https://doi.org/10.3390/math8091510

AMA Style

Ovalle-Magallanes E, Avina-Cervantes JG, Cruz-Aceves I, Ruiz-Pinales J. Transfer Learning for Stenosis Detection in X-ray Coronary Angiography. Mathematics. 2020; 8(9):1510. https://doi.org/10.3390/math8091510

Chicago/Turabian Style

Ovalle-Magallanes, Emmanuel, Juan G. Avina-Cervantes, Ivan Cruz-Aceves, and Jose Ruiz-Pinales. 2020. "Transfer Learning for Stenosis Detection in X-ray Coronary Angiography" Mathematics 8, no. 9: 1510. https://doi.org/10.3390/math8091510

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