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https://hdl.handle.net/11499/56536
Title: | An Approach to Classifying Breast Density Level Using Deep Learning-Based Segmentation Model on Full-Field Digital Mammograms |
Authors: | Ozic, M.U. Yilmaz, A.S. Sandiraz, H.I. Estanto, B.H. |
Keywords: | Breast Density Classification Deep Learning Mammography YOLOv8 Classification (of information) Collagen Deep learning E-learning Image segmentation Muscle Risk assessment X ray screens Breast density Breast density classifications Craniocaudal Deep learning Density levels Learning-based segmentation Mediolateral obliques Pectoral muscles Segmentation models YOLOv8 Mammography |
Publisher: | Institute of Electrical and Electronics Engineers Inc. |
Abstract: | Breast density is a structure that determines the ratio of glandular and connective tissue in a woman's breast, directly affecting the assessment of breast cancer risk in radiological images. According to the Breast Imaging and Reporting Data System, it is classified on an international scale as follows: fatty (A), scattered density (B), heterogeneously dense (C), and extremely dense (D). The specified density categories can be examined in four views from a single patient, which are Right Craniocaudal, Right Mediolateral Oblique, Left Craniocaudal, and Left Mediolateral Oblique, using low-dose X-ray-based mammography. One of the major challenges in artificial intelligence-based breast density classification studies is the presence of the pectoral muscle, which affects the results in Right Mediolateral Oblique and Left Mediolateral Oblique views. Therefore, pre-processing methods are used to attempt automatic or semi-automatic removal of pectoral muscle from the images. This study proposes an approach for breast density classification using four-view mammograms from the VinDr-Mammo dataset, with the YOLOv8 segmentation module, while ensuring that the pectoral muscle is not excluded from the image. As a result of the study, successful breast density classification was achieved without removing the pectoral muscle, and the advantages and disadvantages of the system were discussed. © 2023 IEEE. |
Description: | 7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023 -- 26 October 2023 through 28 October 2023 -- 194332 |
URI: | https://doi.org/10.1109/ISMSIT58785.2023.10304947 https://hdl.handle.net/11499/56536 |
ISBN: | 9798350342154 |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection Teknoloji Fakültesi Koleksiyonu |
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