dc.contributor.author | Dogan, Ramazan Ozgur | |
dc.contributor.author | Ture, Hayati | |
dc.contributor.author | Kayikcioglu, Temel | |
dc.date.accessioned | 2023-02-13T12:50:02Z | |
dc.date.available | 2023-02-13T12:50:02Z | |
dc.date.issued | 2022 | en_US |
dc.identifier.citation | MLO Mamografi Görüntülerinde Pektoral Kas Bölgesinin Backboned U-Net ile Segmentasyonu [Mamografi Görüntülerindeki Pektoral Kaş Bölgesinin Omurgali U-Net ile Bölütlenmesi] Doğan, Ramazan ÖzgürA Doğan RO'ya mail gönder;Türe, HayatiA Ture H'ye posta gönder;Kayıkçıoğlu, TemelB Kayıkçıoğlu T.'ye mail gönder Tümünü yazar listesine kaydet
a Gümüşhane Üniversitesi, Yazılım Mühendisliǧi Bölümü, Gümüşhane, Türkiye b Karadeniz Teknik Üniversitesi, Elektrik Ve Elektronik Mühendisliǧi Bölümü, Trabzon, Türkiye | en_US |
dc.identifier.uri | https://www.mdpi.com/1911-8074/15/9/391 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12440/5843 | |
dc.description.abstract | The pectoral muscle region on MLO mammography images appears prominently similar to suspicious areas. For this reason, Computer-Aided Detection (CAD) systems remove this region to reduce false-positive rates in the mass detection process. In some cases, the pectoral muscle region is exposed to distortions due to the superposition effects caused by the mammography technique. As a result, segmentation error rates of the pectoral muscle region, whose characteristic features are deteriorated, appear. In this study, a method to identify impaired pectoral muscle regions with MobileNetV2 backboned U-Net Deep Learning method is proposed. The proposed method was tested on 84 and 201 mammography images taken from both MIAS and InBreast databases and segmented with 1.81% and 1.92% false-negative (FN) and 0.25% and 0.37% false positive (FP) rates, respectively. Particularly for distorted pectoral muscle regions, the proposed method has been shown to outperform some pioneering studies in this area. © 2022 IEEE. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.relation.ispartof | 2022 30th Signal Processing and Communications Applications Conference, SIU 2022 | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Deformed Pectoral Muscle | en_US |
dc.subject | Mammography | en_US |
dc.subject | MobileNetV2 | en_US |
dc.subject | U-Net | en_US |
dc.title | Segmentation of Pectoral Muscle Region in MLO Mammography Images by Backboned U-Net [Mamografi Görüntülülerindeki Pektoral Kas Bölgesinin Omurgali U-Net ile Bölütlenmesi] | en_US |
dc.type | article | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümü | en_US |
dc.authorid | 0000-0001-6415-5755 | en_US |
dc.authorid | 0000-0003-3012-8016 | en_US |
dc.identifier.doi | 10.1109/SIU55565.2022.9864865 | en_US |
dc.authorwosid | GLN-8177-2022 | en_US |
dc.authorwosid | DWM-9131-2022 | en_US |
dc.authorscopusid | 56247021800 | en_US |
dc.authorscopusid | 36783579900 | en_US |