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 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.
Highlights
Coronary artery disease is produced by plaque buildup in the arterial walls reducing blood flow
Coronary artery disease is the most common type of Cardiovascular Diseases (CVDs), which are the main causes of global deaths, taking out an estimated 17.9 million lives every year, according to the World Health Organization [2]
A custom Convolutional Neural Network (CNN) proposed by Antczak and Liberadzki [18] for stenosis detection was taken as a baseline measurement
Summary
Coronary artery disease is produced by plaque buildup in the arterial walls reducing blood flow. Coronary arteries exchange blood from the heart to vital parts of the human body Such plaques are made up mainly of cholesterol and other waste products from cells. Those adipose depots cause stenosis, an unnatural narrowing of coronary arteries over time, which can partially or entirely block out the blood flow [1]. The core of Convolutional Neural Networks (CNNs) are layers that can extract local features (e.g., edges) across a set of input images through convolution kernels. These layers are known as convolutional layers.
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