Scopus
YÖKSİS Eşleşti
Automated stenosis detection in coronary artery disease using yolov9c: Enhanced efficiency and accuracy in real-time applications
Journal of Real-Time Image Processing · Ekim 2024
YÖKSİS Kayıtları
Automated stenosis detection in coronary artery disease using yolov9c: Enhanced efficiency and accuracy in real-time applications
Journal of Real-Time Image Processing · 2024 SCI-Expanded
PROFESÖR ŞAKİR TAŞDEMİR →
Makale Bilgileri
DergiJournal of Real-Time Image Processing
Yayın TarihiEkim 2024
Cilt / Sayfa21
Scopus ID2-s2.0-85205677848
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 3.5 h, and training time to 11 h. When the same dataset was tested using another significant diagnostic algorithm, SSD MobileNet V1, the YOLOv9c model outperformed it by achieving 1.36 × better F1-score and 1.42 × better mAP@50. These results indicate that the developed YOLOv9c algorithm can provide highly accurate and real-time results for stenosis detection.
Yazarlar (4)
1
Muhammet Akgül
ORCID: 0000-0001-9947-713X
2
Hasan İbrahim Kozan
ORCID: 0000-0002-2453-1645
3
Hasan Ali Akyürek
ORCID: 0000-0002-0520-9888
4
Şakir Taşdemir
Anahtar Kelimeler
Coronary artery disease
Machine learning
Medical imaging
Stenosis detection
YOLOv9c object detection
Kurumlar
Necmettin Erbakan Üniversitesi
Meram Turkey
Selçuk Üniversitesi
Selçuklu Turkey