Şahin,SinanAraz, NusretBakırman, TolgaÇakar, TunaKulavuz, BahadırBayram, BülentÇavuşoğlu, Mustafa2024-06-212024-06-2120240094-243Xhttps://hdl.handle.net/20.500.11779/2291https://doi.org/10.1063/5.0193021Breast cancer is one of the most common types of cancer among women. Early diagnosis of breast cancer has vital importance to prevent unexpected losses. A worldwide effort has been made to tackle early detection challenge. Dynamic contrast-enhanced magnetic resonance imaging is a superior imaging system that improves breast cancer diagnosis quality of physicians. Computer Aided Diagnosis systems are used as a complementary tool to improve breast cancer diagnosis. In last decades, various computer aided diagnosis systems have been proposed. However, the state-of-the-art deep learning-based approaches have started to overcome conventional medical image processing methods. In this study, we aimed to detect malignant breast lesions from open access dynamic contrast-enhanced magnetic resonance imagery dataset using most recent YOLOv7 deep learning architecture. 2400 images have been used for training (80%) and testing (20%) of the network. The metrics calculated with the test dataset are 98.54%, 96.42% and 84.40% for mAP@0.50 IoU, mAP@0.75 IoU and mAP, respectively. The results show that YOLOv7 architecture is capable to detect malignant breast lesions from dynamic contrast-enhanced magnetic resonance images efficiently. © 2024 Author(s).eninfo:eu-repo/semantics/closedAccess[no keyword available]Conference Object10.1063/5.01930212-s2.0-85189248232N/AQ4130300