Automated stenosis detection in coronary artery disease using yolov9c: Enhanced efficiency and accuracy in real-time applications
Yazarlar (4)
Muhammet Akgül Necmettin Erbakan Üniversitesi, Türkiye
Dr. Öğr. Üyesi Hasan İbrahim Kozan Necmettin Erbakan Üniversitesi, Türkiye
Dr. Öğr. Üyesi Hasan Ali Akyürek Necmettin Erbakan Üniversitesi, Türkiye
Prof. Dr. Şakir TAŞDEMİR Sinop Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Journal of Real Time Image Processing (Q2) | ||
| Dergi ISSN | 1861-8200 Dergi Bilgileri (2024) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 09-2024 |
| Cilt / Sayı / Sayfa | 21 / 5 / 177–0 | DOI | 10.1007/s11554-024-01558-x |
| Makale Linki | https://doi.org/10.1007/s11554-024-01558-x | ||
| UAK Araştırma Alanları |
Yapay Zeka
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| Özet |
| Coronary artery disease (CAD) is a prevalent cardiovascular condition and a leading cause of mortality. An accurate and timely diagnosis of CAD is crucial for treatment. This study aims to detect stenosis in real-time and automatically during angiographic imaging for CAD diagnosis, using the YOLOv9c model. A dataset comprising 8325 grayscale images was utilized, sourced from 100 patients diagnosed with one-vessel CAD. To enhance sensitivity and accuracy during the training, testing, and validation phases of stenosis detection, fine-tuning and augmentations were applied. The Python API, utilizing YOLO and Ultralytics libraries, was employed for these processes. The analysis revealed that the YOLOv9c model achieved remarkably high performance in both processing speed and detection accuracy, with an F1-score of 0.99 and mAP@50 of 0.99. The inference time was reduced to 18 ms, fine-tuning time to 3 … |
| Anahtar Kelimeler |
| Coronary artery disease | Machine learning | Medical imaging | Stenosis detection | YOLOv9c object detection |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 4 |
| Scopus | 6 |
| Google Scholar | 11 |