EVALUATION OF U-Net AND ResNet ARCHITECTURES FOR BIOMEDICAL IMAGE SEGMENTATION

dc.contributor.authorÇalışan, Mücahit
dc.contributor.authorGündüzalp, Veysel
dc.contributor.authorOlgun, Nevzat
dc.date.accessioned2024-08-04T19:51:37Z
dc.date.available2024-08-04T19:51:37Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractMedical professionals need methods that provide reliable information in diagnosing and monitoring neurological diseases. Among such methods, studies based on medical image analysis are essential among the active research topics in this field. Tumor segmentation is a popular area, especially with magnetic resonance imaging (MRI). Early diagnosis of tumours plays an essential role in the treatment process. This situation also increases the survival rate of the patients. Manually segmenting a tumour from MR images is a difficult and time-consuming task within the anatomical knowledge of medical professionals. This has necessitated the need for automatic segmentation methods. Convolutional neural networks (CNN), one of the deep learning methods that provide the most advanced results in the field of tumour segmentation, play an important role. This study, tumor segmentation was performed from brain and heart MR images using CNN-based U-Net and ResNet50 deep network architectures. In the segmentation process, their performance was tested using Dice, Sensitivity, PPV and Jaccard metrics. High performance levels were sequentially achieved using the U-Net network architecture on brain images, with success rates of approximately 98.47%, 98.1%, 98.85%, and 96.07%en_US
dc.identifier.doi10.46519/ij3dptdi.1366431
dc.identifier.endpage570en_US
dc.identifier.issn2602-3350
dc.identifier.issue3en_US
dc.identifier.startpage561en_US
dc.identifier.trdizinid1217908en_US
dc.identifier.urihttps://doi.org/10.46519/ij3dptdi.1366431
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1217908
dc.identifier.urihttps://hdl.handle.net/11616/89100
dc.identifier.volume7en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofInternational Journal of 3D Printing Technologies and Digital Industryen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleEVALUATION OF U-Net AND ResNet ARCHITECTURES FOR BIOMEDICAL IMAGE SEGMENTATIONen_US
dc.typeArticleen_US

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