Türk, Ömer
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Turk O.
Türk, Ö.
Türk O.
Türk Ö.
Türk, Ö.
Türk O.
Türk Ö.
Job Title
Doç. Dr.
Email Address
omerturk@artuklu.edu.tr
Main Affiliation
Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü
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Turkish CoHE Profile ID
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WoS Researcher ID
Sustainable Development Goals
3
GOOD HEALTH AND WELL-BEING
2
Research Products
Documents
21
Citations
347
h-index
9
Documents
22
Citations
256
Scholarly Output
23
Articles
16
Views / Downloads
145/1268
Supervised MSc Theses
0
Supervised PhD Theses
0
WoS Citation Count
229
Scopus Citation Count
302
WoS h-index
7
Scopus h-index
7
Patents
0
Projects
1
WoS Citations per Publication
9.96
Scopus Citations per Publication
13.13
Open Access Source
6
Supervised Theses
0
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| Journal | Count |
|---|---|
| 2015 23RD SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) | 1 |
| 2017 25TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) | 1 |
| 2017 25th Signal Processing and Communications Applications Conference, SIU 2017 | 1 |
| 2017 INTERNATIONAL ARTIFICIAL INTELLIGENCE AND DATA PROCESSING SYMPOSIUM (IDAP) | 1 |
| 2018 INNOVATIONS IN INTELLIGENT SYSTEMS AND APPLICATIONS CONFERENCE (ASYU) | 1 |
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Article Epileptik EEG Sinyallerinin Sınıflandırılması için Bir Boyutlu Medyan Yerel İkili Örüntü Temelli Öznitelik Çıkarımı(2017) Türk, Ömer; Özerdem, Mehmet SiraçElektroansefalogram (EEG), epilepsi tespitinde yaygın olarak kullanılan önemli bir veri kaynağıdır. Bu çalışmada da Bonn Üniversitesi Epileptoloji bölümü veritabanından alınan ve A, B, C, D, E olmak üzere 5 işaret grubundan oluşan EEG kayıtları kullanılmıştır. Bir boyutklu medyan yerel ikili örüntü (1B-MYİÖ) yöntemi uygulanarak elde edilen özniteliklerin k-En Yakın Komşu (k-NN) sınıflandırıcısı ile sınıflandırılması amaçlanmıştır. Çalışmada geliştirilen 1BMYİÖ yönteminin öznitelik olarak sınıflandırma başarısı değerlendirilmiştir. Bu sınıflandırma için karışıklık matrisi hesaplanarak model başarım ölçümü yapılmıştır. Çalışmada A-E veri setleri için sınıflandırma performansı %100, A-D veri setleri için %99.00, D-E veri setleri için %98.00, E-CD veri setleri için %99.50 ve A-D-E veri setleri için de %96.00 olarak bulunmuştur. Çalışmada kullanılan 1B-MYİÖ yönteminin, literatürde kullanılan birçok yöntemden daha iyi sonuç verdiği görülmüştür.Article Citation - WoS: 8Citation - Scopus: 14Employing deep learning architectures for image-based automatic cataract diagnosis(TÜBİTAK, 2021) Acar, Emrullah; Türk, Ömer; Ertuğrul, Ömer Faruk; Aldemir, ErdoğanVarious eye diseases affect the quality of human life severely and ultimately may result in complete vision loss. Ocular diseases manifest themselves through mostly visual indicators in the early or mature stages of the disease by showing abnormalities in optics disc, fovea, or other descriptive anatomical structures of the eye. Cataract is among the most harmful diseases that affects millions of people and the leading cause of public vision impairment. It shows major visual symptoms that can be employed for early detection before the hypermature stage. Automatic diagnosis systems intend to assist ophthalmological experts by mitigating the burden of manual clinical decisions and on health care utilization. In this study, a diagnosis system based on color fundus images are addressed for cataract disease. Deep learning-based models were performed for the automatic identification of cataract diseases. Two pretrained robust architectures, namely VGGNet and DenseNet, were employed to detect abnormalities in descriptive parts of the human eye. The proposed system is implemented on a wide and unique dataset that includes diverse color retinal fundus images that are acquired comparatively in low-cost and common modality, which is considered a major contribution of the study. The dataset show symptoms of cataracts in different phases and represents the characteristics of the cataract. By the proposed system, dysfunction associated with cataracts could be identified in the early stage. The achievement of the proposed system is compared to various traditional and up-to-date classification systems. The proposed system achieves 97.94% diagnosis rate for cataract disease grading.Article Citation - Scopus: 1A Hybrid 2d Gaussian Filter and Deep Learning Approach With Visualization of Class Activation for Automatic Lung and Colon Cancer Diagnosis(Sage Publications inc, 2024) Turk, Omer; Acar, Emrullah; Irmak, Emrah; Yilmaz, Musa; Bakis, EnesCancer is a significant public health issue due to its high prevalence and lethality, particularly lung and colon cancers, which account for over a quarter of all cancer cases. This study aims to enhance the detection rate of lung and colon cancer by designing an automated diagnosis system. The system focuses on early detection through image pre-processing with a 2D Gaussian filter, while maintaining simplicity to minimize computational requirements and runtime. The study employs three Convolutional Neural Network (CNN) models-MobileNet, VGG16, and ResNet50-to diagnose five types of cancer: Colon Adenocarcinoma, Benign Colonic Tissue, Lung Adenocarcinoma, Benign Lung Tissue, and Lung Squamous Cell Carcinoma. A large dataset comprising 25 000 histopathological images is utilized. Additionally, the research addresses the need for safety levels in the model by using Class Activation Mapping (CAM) for explanatory purposes. Experimental results indicate that the proposed system achieves a high diagnostic accuracy of 99.38% for lung and colon cancers. This high performance underscores the effectiveness of the automated system in detecting these types of cancer. The findings from this study support the potential for early diagnosis of lung and colon cancers, which can facilitate timely therapeutic interventions and improve patient outcomes.Conference Object Citation - WoS: 13Classification of Mental Task EEG Records Using Hjorth Parameters(IEEE, 2017) Turk, Omer; Seker, Mesut; Akpolat, Veysi; Ozerdem, Mchmet SiracThe effects of mental activities on brain dynamics is the main field that studied for a long time, but the results of studies have not reached the desired level. The aim of present study was to classify the mental task EEG records by using Hjorth parameters. hi this study, EEG signals that recorded from 9 subjects were used. EEG signals were recorded by applying a experimental paradigm which contains five stimuli related to different mental task. These stimuli are defined as condition word mental subtraction spatial navigation right hand motor imagery and feet motor imagery Wavelet packet transform was used to obtain sub bands of EEC signals. Statistical parameters that consist of mobility, complexity and Mahalanobis distance were applied to sub-bands. Feature vectors were classified by using artificial neural network. When classification performances related to mental activities were examined, the best classification accuracy was obtained as nearly 80% for 'condition word - mental subtraction', ('spatial navigation feet motor imagery;' and 'spatial navigation - condition word'. The lowest classification accuracy was obtained for 'mental subtraction - right hand motor imagery,', 'condition word - right hand motor imagery' and 'spatial navigation right hand motor imagery'. The classification accuracies related to all stimuli that classifed among themselves were obtained as 77,61%.Article Citation - WoS: 9Citation - Scopus: 10FPGA simulation of chaotic tent map-based S-Box design(Wiley Online Library, 2022) Türk, ÖmerThe chaotic system has a characteristically random behavior by nature, and these systems have their own characteristics in a completely deterministic structure. This feature of a chaotic system makes it difficult to predict encryptions designed based on such a system. Thanks to this unpredictable and strong feature, maps produced from chaotic systems are an important alternative in the field of encryption. One of the structures obtained by employing chaotic maps is the substitution box. S-Box, which provides the confusion principle used in block ciphers, is the main block that dynamically replaces unencrypted data with confidential data and makes a significant contribution to ensuring high security in the encryption system. Therefore, S-Boxes hold a critical role in block ciphers. Speed and reliability are important parameters in the creation of this main block. Especially, applications performed on hardware are more reliable and high performance. Therefore, in this study, an S-Box was designed using fieldprogrammable gate arrays (FPGA) simulation from a chaotic tent map to create a fast and reliable S-Box because FPGAs offer solutions that may be important in this field considering their fast and customizable architecture. In the proposed method, the S-Box was created in 0.16 s. In addition, the dynamic properties of the chaotic tent map were analyzed with Lyapunov exponents, and the NIST SP 800-22 test was applied for the information encryption suitability of the proposed chaotic system. Also, to test the reliability of the produced S-Box structures, SAC, non-linearity, bit independence criteria, and input/output XOR distribution table metrics were implemented. The results showed that the proposed chaotic map was dynamic and passed the reliability tests successfully.Article Citation - WoS: 6Citation - Scopus: 6Can deep learning replace histopathological examinations in the differential diagnosis of cervical lymphadenopathy?(Springer, 2024) Can, Sermin; Türk, Ömer; Ayral, Muhammed; Kozan, Günay; Arı, Hamza; Akdağ, Mehmet; Yıldırım Baylan, MüzeyyenIntroduction: We aimed to develop a diagnostic deep learning model using contrast-enhanced CT images and to investigate whether cervical lymphadenopathies can be diagnosed with these deep learning methods without radiologist interpretations and histopathological examinations. Material method: A total of 400 patients who underwent surgery for lymphadenopathy in the neck between 2010 and 2022 were retrospectively analyzed. They were examined in four groups of 100 patients: the granulomatous diseases group, the lymphoma group, the squamous cell tumor group, and the reactive hyperplasia group. The diagnoses of the patients were confirmed histopathologically. Two CT images from all the patients in each group were used in the study. The CT images were classified using ResNet50, NASNetMobile, and DenseNet121 architecture input. Results: The classification accuracies obtained with ResNet50, DenseNet121, and NASNetMobile were 92.5%, 90.62, and 87.5, respectively. Conclusion: Deep learning is a useful diagnostic tool in diagnosing cervical lymphadenopathy. In the near future, many diseases could be diagnosed with deep learning models without radiologist interpretations and invasive examinations such as histopathological examinations. However, further studies with much larger case series are needed to develop accurate deep-learning models.Conference Object MENTAL ACTIVITY DETECTION FROM EEG RECORDS USING LOCAL BINARY PATTERN METHOD(IEEE, 2017) Turk, Omer; Ozerdem, Mehmet SiracElectroencephalogram signals are widely used in the detection of different activities but not in the desired level. In this study with this motivation, it is aimed to obtain the attributes by using the Local Bilinear Pattern (LBP) method of EEG records for various mental activities and to classify these features by k-Nearest Neighbor (k-NN) method. The binary classification performance of these EEG records containing 5 mental tasks was evaluated. In addition, in order to evaluate classification performance, confusion matrix was used as model performance criterion. In the study, the average of the classification performance of all participants was found as 87.38%. As a model performance criterion from the participants' classification of mental activity, accuracy was 85.03%, precision was 85.40% and sensitivity was 85.47%. So, as a result the obtained results support the literature and the applicability of the LBP method for EEG markings has been confirmed.Article Citation - WoS: 18Citation - Scopus: 20A Class Activation Map-Based Interpretable Transfer Learning Model for Automated Detection of ADHD from fMRI Data(Sage Journals, 2022) Uyulan, Caglar; Erguzel, Turker Tekin; Türk, Ömer; Farhad, Shams; Metin, Bariş; Tarhan, NevzatAutomatic detection of Attention Deficit Hyperactivity Disorder (ADHD) based on the functional Magnetic Resonance Imaging (fMRI) through Deep Learning (DL) is becoming a quite useful methodology due to the curse of-dimensionality problem of the data is solved. Also, this method proposes an invasive and robust solution to the variances in data acquisition and class distribution imbalances. In this paper, a transfer learning approach, specifically ResNet-50 type pre-trained 2D-Convolutional Neural Network (CNN) was used to automatically classify ADHD and healthy children. The results demonstrated that ResNet-50 architecture with 10-k cross-validation (CV) achieves an overall classification accuracy of 93.45%. The interpretation of the results was done via the Class Activation Map (CAM) analysis which showed that children with ADHD differed from controls in a wide range of brain areas including frontal, parietal and temporal lobes.Conference Object Classification of EEG Records for the Cursor Movement with the Convolutional Neural Network [Imleç Hareketine ilişkin EEG Kayitlarinin Evrişimsel Sinir Agi ile Siniflandirilmasi](Institute of Electrical and Electronics Engineers Inc., 2018) Türk O.; Özerdem M.S.Nowadays, very successful results are obtained with deep learning architectures which can be applied to many fields. Because of the high performances it provides in many areas, deep learning has come to a central position in machine learning and pattern recognition. In this study, electroencephalogram (EEG) signals related to up and down cursor movements were represented as image pattern by using obtained approximation coefficients after wavelet transform. The Obtained image patterns were classified by applying Convolutional Neural Network. In this study, EEG records related to cursor movements were classified and classification accuracy was obtained as 88.13%. © 2018 IEEE.Article Citation - WoS: 10Citation - Scopus: 9Identification of cotton and corn plant areas by employing deep transformer encoder approach and different time series satellite images: A case study in Diyarbakir, Turkey(ScienceDirect, 2023) Türk, Ömer; Şimşek Bağcı, Reyhan; Acar, EmrullahIt is very important to determine the crops in the agricultural field in a short time and accurately. Thanks to the satellite images obtained from remote sensing sensors, information can be obtained on many subjects such as the detection and development of agricultural products and annual product forecasting. In this study, it is aimed to automatically detect agricultural crops (corn and cotton) by using Sentinel-1 and Landsat-8 satellite image indexes via a new deep learning approach (Deep Transformer Encoder). This work was carried out in several stages, respectively. In the first stage, a pilot area was determined to obtain Sentinel-1 and Landsat-8 satellite images of agricultural crops used in this study. In the second stage, the coordinates of 100 sample points from this pilot area were taken with the help of GPS and these coordinates were then transferred to Sentinel-1 and Landsat-8 satellite images. In the next step, reflection and backscattering values were obtained from the pixels of the satellite images corresponding to the sample points of these agricultural crops. While creating the data sets of satellite images, the months of June, July, August and September for the years 2016–2021, when the development and harvesting times of agricultural products are close to each other, were preferred. The image data set used in the study consists of a total of 434 images for Sentinel-1 satellite and a total of 693 images for Landsat-8. At the last stage, the datasets obtained from different satellite images were evaluated in three different categories for crop identification with the aid of Deep Transformer Encoder approach. These are: (1-) Crop identification with only Sentinel-1 dataset, (2-) Crop identification only with Landsat-8 dataset, (3-) Crop identification with both Sentinel-1 and Landsat-8 datasets. The results showed that 85%, 95% and 87.5% accuracy values were obtained from the band parameters of Sentinel-1 dataset, Landsat-8 dataset and Sentinel-1&Landsat-8 datasets, respectively
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