| Autor: |
Kulavuz, Bahadir, Cavusoglu, Mustafa, Bayram, Bulent, Bakirman, Tolga, Sahin, Sinan, Araz, Nusret, Orhan, Gozde, Surmeli, Hulusi Emre, Cakar, Tuna |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 3030 Issue 1, p1-5, 5p |
| Abstrakt: |
Breast 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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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