Yılmaz, Vadi Su

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Profile URL
https://hdl.handle.net/20.500.14411/7252
Name Variants
Y., Vadi Su
Yilmaz,V.S.
Vadi Su, Yılmaz
V. S. Yilmaz
Yılmaz,V.S.
Vadi Su, Yilmaz
V.S.Yilmaz
Yilmaz, Vadi Su
V.,Yilmaz
Y.,Vadi Su
Yilmaz V.
Yılmaz, Vadi Su
V., Yilmaz
V.,Yılmaz
V.S.Yılmaz
V. S. Yılmaz
Job Title
Araştırma Görevlisi
Email Address
vadi.yilmaz@atilim.edu.tr
Main Affiliation
Electrical-Electronics Engineering
Mechatronics Engineering
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Website
ORCID ID
Scopus Author ID
57201855036
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
EGI-3182-2022

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7

Citations

30

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3

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Documents

7

Citations

20

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Scholarly Output

11

Articles

4

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8/22

Supervised MSc Theses

1

Supervised PhD Theses

1

WoS Citation Count

20

Scopus Citation Count

30

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0

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0

WoS Citations per Publication

1.82

Scopus Citations per Publication

2.73

Open Access Source

3

Supervised Theses

2

JournalCount
10th International Conference on Electrical and Electronics Engineering (ELECO) -- NOV 30-DEC 02, 2017 -- Bursa, TURKEY1
2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings -- 28th Signal Processing and Communications Applications Conference, SIU 2020 -- 5 October 2020 through 7 October 2020 -- Gaziantep -- 1664131
24th IEEE International Conference on Electronics, Circuits and Systems (ICECS) -- DEC 05-08, 2017 -- Batumi, GEORGIA1
28th Signal Processing and Communications Applications Conference (SIU) -- OCT 05-07, 2020 -- ELECTR NETWORK1
Diagnostics1
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Author: Akdag, MetehanHas files: true

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    Article
    Citation - WoS: 4
    Citation - Scopus: 7
    Investigating the Impact of Two Major Programming Environments on the Accuracy of Deep Learning-Based Glioma Detection From Mri Images
    (Mdpi, 2023) Yilmaz, Vadi Su; Akdag, Metehan; Dalveren, Yaser; Doruk, Resat Ozgur; Kara, Ali; Soylu, Ahmet
    Brain tumors have been the subject of research for many years. Brain tumors are typically classified into two main groups: benign and malignant tumors. The most common tumor type among malignant brain tumors is known as glioma. In the diagnosis of glioma, different imaging technologies could be used. Among these techniques, MRI is the most preferred imaging technology due to its high-resolution image data. However, the detection of gliomas from a huge set of MRI data could be challenging for the practitioners. In order to solve this concern, many Deep Learning (DL) models based on Convolutional Neural Networks (CNNs) have been proposed to be used in detecting glioma. However, understanding which CNN architecture would work efficiently under various conditions including development environment or programming aspects as well as performance analysis has not been studied so far. In this research work, therefore, the purpose is to investigate the impact of two major programming environments (namely, MATLAB and Python) on the accuracy of CNN-based glioma detection from Magnetic Resonance Imaging (MRI) images. To this end, experiments on the Brain Tumor Segmentation (BraTS) dataset (2016 and 2017) consisting of multiparametric magnetic MRI images are performed by implementing two popular CNN architectures, the three-dimensional (3D) U-Net and the V-Net in the programming environments. From the results, it is concluded that the use of Python with Google Colaboratory (Colab) might be highly useful in the implementation of CNN-based models for glioma detection. Moreover, the 3D U-Net model is found to perform better, attaining a high accuracy on the dataset. The authors believe that the results achieved from this study would provide useful information to the research community in their appropriate implementation of DL approaches for brain tumor detection.