Browsing by Author "Araz, Nusret"

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    Breast Lesion Detection From Dce-Mri Using Yolov7
    (American Institute of Physics, 2024) Şahin,Sinan; Araz, Nusret; Bakırman, Tolga; Çakar, Tuna; Kulavuz, Bahadır; Bayram, Bülent; Çavuşoğlu, Mustafa
    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. © 2024 Author(s).
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    Development of a Knowledge-Based Multimodal Deep Learning System for Automatic Breast Lesion Segmentation and Diagnosis in Mg/Dmr Images
    (IEEE, 2023) Orhan, Gözde; Çavuşoğlu, Mustafa; Sürmeli, Hulusi Emre; Çakar, Tuna; Araz, Nusret; Bayram, Bülent
    Deep learning networks (DLNs) rely on labeled training datasets as their fundamental building blocks. While various databases exist worldwide, there is currently no domestic solution available in our country. This project aims to create a domestic database by automatically segmenting breast lesions in MG/DMR images based on their types and developing a knowledge-based multimodal DL-based integrated computer-aided diagnosis system to analyze the images, thereby providing the system with continuous learning capability. Different brands of devices exist for MG/DMR, necessitating the multimodal operation of image processing/artificial intelligence algorithms. To achieve this goal, the network was trained first, and then prelearned data were transferred to enable the training of data from different networks once accurate results are obtained. The developed system has the potential to enable the automatic detection of breast lesions, ensuring fast and high diagnostic accuracy. Additionally, it might also facilitate the retrospective analysis of patients' periodic check-up results.