2026-10-24T15:30:13Zhttps://gumushane-rdlopen.rawdatalibrary.net/oai/request

oai:gumushane-rdlopen.rawdatalibrary.net:gumushane/159892026-10-22T11:38:20Zopenairecom_gumushane_10000col_gumushane_10001com_gumushane_10007col_gumushane_10171

Segmentation of Pectoral Muscle Region in MLO Mammography Images by Backboned U-NetDogan, Ramazan OzgurTüre, HayatiKayikçioglu, TemelIEEEMammographyDeformed Pectoral MuscleU-NetMobileNetV2Deep LearningThe 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.IEEE2022Conference Objecthttps://rdlopen.rawdatalibrary.net/r/gumushane/15989https://doi.org/10.1109/siu55565.2022.9864865978-1-6654-5092-82165-0608tr2022 30TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE, SIUinfo:eu-repo/semantics/closedAccessinfo:eu-repo/semantics/conferenceObjecthttps://gumushane-rdlopen.rawdatalibrary.net/tr/items/gumushane/15989