DeepImageSegmentationApp: Deep Learning Application for Image Segmentation

Lütfü Bayrak; Kenan Koçkaya; Ahmet Çınar

Makale detayları

DeepImageSegmentationApp: Deep Learning Application for Image Segmentation

Yazarlar:

Lütfü Bayrak | Kenan Koçkaya | Ahmet Çınar

Yayıncı:

Ordu University

Ana Başlıklar:

Derin Öğrenme

Dergi:

Ordu Üniversitesi Bilim ve Teknoloji Dergisi Vol.15 Issue 1, pp.88-100

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001352231

pISSN:

2146-6440

Anahtar kelimeler:

Özet:

 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.comlbayrakDeepImageSegmentationApp.

 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.comlbayrakDeepImageSegmentationApp.

 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.comlbayrakDeepImageSegmentationApp.

 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 Kaggledan 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.comlbayrakDeepImageSegmentationApp.

Makale detayları

DeepImageSegmentationApp: Deep Learning Application for Image Segmentation

Yazarlar:

Lütfü Bayrak | Kenan Koçkaya | Ahmet Çınar

Yayıncı:

Ordu University

Ana Başlıklar:

Derin Öğrenme

Dergi:

Ordu Üniversitesi Bilim ve Teknoloji Dergisi Vol.15 Issue 1, pp.88-100

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001352231

pISSN:

2146-6440

Image Segmentation

Deep Learning

U-Net

V-NetGörüntü Bölümlendirme

Derin Öğrenme

U-Net

V-Net

Sayı

Makale detayları

Yazarlar: Lütfü Bayrak | Kenan Koçkaya | Ahmet Çınar

Yayıncı: Ordu University

Ana Başlıklar: Derin Öğrenme

Dergi: Ordu Üniversitesi Bilim ve Teknoloji Dergisi Vol.15 Issue 1, pp.88-100

Belge Tipi: Makale

Yayın Tarihi: 6/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001352231

pISSN: 2146-6440

Sayı

Yazarlar:

Lütfü Bayrak | Kenan Koçkaya | Ahmet Çınar

Yayıncı:

Ordu University

Ana Başlıklar:

Derin Öğrenme

Dergi:

Ordu Üniversitesi Bilim ve Teknoloji Dergisi Vol.15 Issue 1, pp.88-100

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001352231

pISSN:

2146-6440