TY  - JOUR
T1  - DeepImageSegmentationApp: Deep Learning Application for Image Segmentation
TT  - DeepImageSegmentationApp: Görüntü Segmentasyonu için Derin Öğrenme Uygulaması
AU  - Koçkaya, Kenan
AU  - Bayrak, Lütfü
AU  - Çınar, Ahmet
PY  - 2025
DA  - June
Y2  - 2025
DO  - 10.54370/ordubtd.1651665
JF  - Ordu Üniversitesi Bilim ve Teknoloji Dergisi
JO  - Ordu Üniv. Bil. Tek. Derg.
PB  - Ordu Üniversitesi
WT  - DergiPark
SN  - 2146-6440
SP  - 88
EP  - 100
VL  - 15
IS  - 1
LA  - en
AB  - There are many methods to examine a specific region or object in images. One of the most important of these methods is image segmentation. Image segmentation involves dividing images (or video frames) into multiple sections or objects. There are many different model architectures developed in the field of image segmentation.In this study, a deep learning-based image segmentation application interface has been developed. The performance of the proposed application has been analyzed on the COVID-19 dataset obtained from Kaggle. The performance results of the application are presented in a comparative analysis of the U-NET and V-NET models, which are known for their accuracy, for various system parameters. In the analysis results, it is seen that the V-NET architecture is better than the U-NET architecture. The developed application environment has revealed the difference between the models and the usability of the application environment. This standalone software can be downloaded at: https://github.com/lbayrak/DeepImageSegmentationApp.
KW  - Image Segmentation
KW  - Deep Learning
KW  - U-Net
KW  - V-Net
N2  - Görüntülerde belirli bir bölgeyi veya nesneyi incelemek için birçok yöntem vardır. Bu yöntemlerin en önemlilerinden biri görüntü segmentasyonudur. Görüntü segmentasyonu, görüntüleri (veya video karelerini) birden fazla bölüme veya nesneye ayırmayı içerir. Görüntü segmentasyonu alanında geliştirilen birçok farklı model mimarisi vardır. Bu çalışmada, derin öğrenme tabanlı bir görüntü segmentasyonu uygulama arayüzü geliştirilmiştir. Önerilen uygulamanın performansı Kaggle'dan elde edilen Covid19 veri kümesi üzerinde analiz edilmiştir. Uygulamanın performans sonuçları, farklı sistem parametreleri için bilinen doğrulukla U-NET ve V-NET modelleri üzerinde karşılaştırmalı olarak sunulmuştur. Analiz sonuçlarında V-NET mimarisinin U-NET mimarisine göre daha iyi olduğu açıkça görülmektedir. Geliştirilen uygulama ortamı modeller arasındaki farkı ve uygulama ortamının kullanışlılığını ortaya koymuştur. Bu bağımsız yazılım şu adresten indirilebilir: https://github.com/lbayrak/DeepImageSegmentationApp.
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UR  - https://doi.org/10.54370/ordubtd.1651665
L1  - https://dergipark.org.tr/tr/download/article-file/4662686
ER  -