ArticleDiagnostics (Basel, Switzerland)2023
The Application of Deep Learning for the Segmentation and Classification of Coronary Arteries.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 41 citations in OpenAlex.
- A neighborhood attention transformer network for enhanced 3D segmentation of the left anterior descending artery.Medical physics · 2026Article
- Coronary artery stenosis segmentation using U-Net architecture with customised loss function.Scientific reports · 2026Article
- A Machine Learning Driven Approach to Quantifying Coronary Artery Tortuosity.JACC. Advances · 2026Article
- VMamba-QAG-net: a five-stage pipeline for SYNTAX score computation and decision support in interventional cardiology using X-ray angiography.Frontiers in artificial intelligence · 2026Article
- Artificial Intelligence in Biomedicine: A Systematic Review from Nanomedicine to Neurology and Hepatology.Pharmaceutics · 2025Review
- Validation of UniverSeg for Interventional Abdominal Angiographic Segmentation.Journal of imaging informatics in medicine · 2025Article
- Article
- Grey wolf optimization technique with U-shaped and capsule networks-A novel framework for glaucoma diagnosis.MethodsX · 2025Article
- Deep learning-based classification of coronary arteries and left ventricle using multimodal data for autonomous protocol selection or adjustment in angiography.Scientific reports · 2025Article
- Review
- Optimized Lightweight Architecture for Coronary Artery Disease Classification in Medical Imaging.Diagnostics (Basel, Switzerland) · 2025Article
- Patient-specificFrontiers in cardiovascular medicine · 2024Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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).
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