The Application of Deep Learning for the Segmentation and Classification of Coronary Arteries

• Published in: Diagnostics (Basel) Vol. 13; no. 13; p. 2274
• Main Authors: Kaba, Şerife, Haci, Huseyin, Isin, Ali, Ilhan, Ahmet, Conkbayir, Cenk
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.07.2023; MDPI
• Subjects: Accuracy; Angiography; Arteries; Automation; Blood vessels; Cardiology; Cardiovascular disease; Catheters; Classification; coronary arteries; coronary artery disease (CAD); Coronary heart disease; Coronary vessels; Decision making; Deep learning; Diagnosis; Heart; Medical imaging; Medical imaging equipment; pretrained models; U-Net; Cyprus; Taiwan; coronary artery disease (CAD); coronary arteries; pretrained models; U-Net; angiography
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics13132274

In recent years, the prevalence of coronary artery disease (CAD) has become one of the leading causes of death around the world. Accurate stenosis detection of coronary arteries is crucial for timely treatment. Cardiologists use visual estimations when reading coronary angiography images to diagnose stenosis. As a result, they face various challenges which include high workloads, long processing times and human error. Computer-aided segmentation and classification of coronary arteries, as to whether stenosis is present or not, significantly reduces the workload of cardiologists and human errors caused by manual processes. Moreover, deep learning techniques have been shown to aid medical experts in diagnosing diseases using biomedical imaging. Thus, this study proposes the use of automatic segmentation of coronary arteries using U-Net, ResUNet-a, UNet++, models and classification using DenseNet201, EfficientNet-B0, Mobilenet-v2, ResNet101 and Xception models. In the case of segmentation, the comparative analysis of the three models has shown that U-Net achieved the highest score with a 0.8467 Dice score and 0.7454 Jaccard Index in comparison with UNet++ and ResUnet-a. Evaluation of the classification model's performances has shown that DenseNet201 performed better than other pretrained models with 0.9000 accuracy, 0.9833 specificity, 0.9556 PPV, 0.7746 Cohen's Kappa and 0.9694 Area Under the Curve (AUC).