Segmentation of Pectoral Muscle Region in MLO Mammography Images by Backboned U-Net [Mamografi Görüntülülerindeki Pektoral Kas Bölgesinin Omurgali U-Net ile Bölütlenmesi]
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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, TürkiyeAbstract
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.
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