Profile URL: https://hdl.handle.net/20.500.13091/11614
Job Title:Prof. Dr.
Email Address:rceylan@ktun.edu.tr
Main Affiliation:02.04. Department of Electrical and Electronics Engineering
Status: Current Staff
ORCID:
0000-0003-0294-5692
0000-0003-0294-5692Scopus ID:
12244684600
12244684600YÖK Akademik: 8F6116B07E5ABCF2
Google Scholar:
hPqYj_MAAAAJ
hPqYj_MAAAAJWeb of Science ID:
HJK-5634-2023
HJK-5634-202333 results
Scholarly Output Search Results
Now showing 1 - 10 of 33
Article Citation - WoS: 10Citation - Scopus: 10The Effect of Dictionary Learning on Weight Update of Adaboost and Ecg Classification(ELSEVIER, 2020-12-01) Barstuğan, Mücahid; Ceylan, RahimeA signal can be represented by sparse representation with fewer coefficients. Due to this ability, sparse representation is used in research fields such as signal compression, noise elimination, and classification. In this study, sparse coefficients of the signals were obtained by using dictionary learning and sparse representation algorithms. The obtained coefficients were used in the weight update process of three different classifiers, which were created by using AdaBoost, SVM, and LDA algorithms. So, Dictionary learning based AdaBoost classifiers were obtained. The proposed Dictionary Learning (DL) based AdaBoost classifiers classified the ECG (Electrocardiography) signals. Before classification, the feature selection process was applied to ECG signals and six different feature subsets were obtained by Discrete Wavelet Transform (DWT), First Order Statistics (FOS), T-test, Bhattacharyya, Entropy, and Wilcoxon test methods. The feature subsets were used as the new dataset. The classification process was done by the proposed method and satisfying results were obtained. The best classification accuracy was obtained as 99.75% by the proposed dictionary learning based method called as DL-AdaBoost-SVM on feature subsets obtained by DWT and Wilcoxon test methods. (C) 2018 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.Article Citation - WoS: 42Citation - Scopus: 49A Pso Based Approach: Scout Particle Swarm Algorithm for Continuous Global Optimization Problems(OXFORD UNIV PRESS, 2018) Koyuncu, Hasan; Ceylan, RahimeIn the literature, most studies focus on designing new methods inspired by biological processes, however hybridization of methods and hybridization way should be examined carefully to generate more suitable optimization methods. In this study, we handle Particle Swarm Optimization (PSO) and an efficient operator of Artificial Bee Colony Optimization (ABC) to design an efficient technique for continuous function optimization. In PSO, velocity and position concepts guide particles to achieve convergence. At this point, variable and stable parameters are ineffective for regenerating awkward particles that cannot improve their personal best position (P-best). Thus, the need for external intervention is inevitable once a useful particle becomes an awkward one. In ABC, the scout bee phase acts as external intervention by sustaining the resurgence of incapable individuals. With the addition of a scout bee phase to standard PSO, Scout Particle Swarm Optimization (ScPSO) is formed which eliminates the most important handicap of PSO. Consequently, a robust optimization algorithm is obtained. ScPSO is tested on constrained optimization problems and optimum parameter values are obtained for the general use of ScPSO. To evaluate the performance, ScPSO is compared with Genetic Algorithm (GA), with variants of the PSO and ABC methods, and with hybrid approaches based on PSO and ABC algorithms on numerical function optimization. As seen in the results, ScPSO results in better optimal solutions than other approaches. In addition, its convergence is superior to a basic optimization method, to the variants of PSO and ABC algorithms, and to the hybrid approaches on different numerical benchmark functions. According to the results, the Total Statistical Success (TSS) value of ScPSO ranks first (5) in comparison with PSO variants; the second best TSS (2) belongs to CLPSO and SP-PSO techniques. In a comparison with ABC variants, the best TSS value (6) is obtained by ScPSO, while TSS of BitABC is 2. In comparison with hybrid techniques, ScPSO obtains the best Total Average Rank (TAR) as 1.375, and TSS of ScPSO ranks first (6) again. The fitness values obtained by ScPSO are generally more satisfactory than the values obtained by other methods. Consequently, ScPSO achieve promising gains over other optimization methods; in parallel with this result, its usage can be extended to different working disciplines. (C) 2018 Society for Computational Design and Engineering. Publishing Services by Elsevier.Article Citation - WoS: 2Citation - Scopus: 2Adrenal Lesion Classification With Abdomen Caps and the Effect of Roi Size(Springer, 2023-04-25) Solak, Ahmet; Ceylan, Rahime; Bozkurt, Mustafa Alper; Cebeci, Hakan; Koplay, MustafaAccurate classification of adrenal lesions on magnetic resonance (MR) images are very important for diagnosis and treatment planning. The detection and classification of lesions in medical imaging heavily rely on several key factors, including the specialist's level of experience, work intensity, and fatigue of the clinician. These factors are critical determinants of the accuracy and effectiveness of the diagnostic process, which in turn has a direct impact on patient health outcomes. With the spread of artificial intelligence, the use of computer-aided diagnosis (CAD) systems in disease diagnosis has also increased. In this study, adrenal lesion classification was performed using deep learning on MR images. The data set used was obtained from the Department of Radiology, Faculty of Medicine, Selcuk University, and all adrenal lesions were identified and reviewed in consensus by two radiologists experienced with abdominal MR. Studies were carried out on two different data sets created by T1- and T2-weighted MR images. The data set consisted of 112 benign and 10 malignant lesions for each mode. Experiments were performed with regions of interest (ROIs) of different sizes to increase the working performance. Thus, the effect of the selected ROI size on the classification performance was assessed. In addition, instead of the convolutional neural network (CNN) models used in deep learning, a unique classification model structure called Abdomen Caps was proposed. When the data sets used in classification studies are manually separated for training, validation, and testing, different results are obtained with different data sets for each stage. To eliminate this imbalance, tenfold cross-validation was used in this study. The best results obtained were 0.982, 0.999, 0.969, 0.983, 0.998, and 0.964 for accuracy, precision, recall, F1-score, area under the curve (AUC) score, and kappa score, respectively.Conference Object Citation - WoS: 1Citation - Scopus: 1Classification of Mammography Images by Transfer Learning(IEEE, 2020) Solak, Ahmet; Ceylan, RahimeBreast cancer is the most common cancer type in women worldwide. Diagnosis and early detection of cancer by mammography images are of great importance in cancer treatment. The use of deep learning in Computer Assisted Diagnostic systems has gained a great momentum especially since 2012. In this study, benign and malignant mass images were reproduced with data augmentation and the data sets obtained were classified with deep learning networks. In this study, a scratch Convolutional Neural Network (CNN) architecture was created and transfer learning was realized with different network models which trained on IMAGENET images. In the transfer learning section, separate training results were obtained by performing feature extraction and fine tuning of network parameters. As a result of the study, the best results were obtained with MobileNet, NASNetLarge and InceptionResNetV2 models which are used in transfer learning models.Conference Object Citation - WoS: 1Citation - Scopus: 8Pancreas Segmentation in Abdominal Ct Images With U-Net Model(IEEE, 2020) Kurnaz, Ender; Ceylan, RahimePancreas is one of the most challenging organs in segmentation due to its different shape, position and size in each human being. With the development of machine learning, various deep learning methods are applied to segment the pancreas among organs in the abdominal region. In this study, pancreas segmentation is performed using the U-Net model, which is one of the convolutional neural networks (CNN) models. The results of pancreas segmentation performed on the Pancreas CT data set obtained from The Cancer Imaging Archive (TCIA) database containing computed tomography images of 82 patients are presented in detail. According to the results, Dice similarity coefficient and Jaccard similarity coefficient are found to be 0.78 and 0.66, respectively.Publication Pancreas Segmentation in Abdominal CT Images with U-Net Model(2020) Kurnaz, Ender; Ceylan, RahimePancreas is one of the most challenging organs in segmentation due to its different shape, position and size in each human being. With the development of machine learning, various deep learning methods are applied to segment the pancreas among organs in the abdominal region. In this study, pancreas segmentation is performed using the U-Net model, which is one of the convolutional neural networks (CNN) models. The results of pancreas segmentation performed on the Pancreas CT data set obtained from The Cancer Imaging Archive (TCIA) database containing computed tomography images of 82 patients are presented in detail. According to the results, Dice similarity coefficient and Jaccard similarity coefficient are found to be 0.78 and 0.66, respectively.Master Thesis Abdomen Bt Görüntülerinde Pankreas Segmentasyonu için Yeni Bir Derin Öğrenme Yaklaşımı: Pascal U-net(Konya Teknik Üniversitesi, 2021) Kurnaz, Ender; Ceylan, RahimeGünümüzde derin öğrenme modellerinin medikal görüntü işlemede kullanımı hız kazanmıştır. Özellikle kesit görüntülerinden organ segmentasyonu üzerine gerçekleştirilen çalışmalarda derin öğrenme yöntemleri sıklıkla tercih edilmektedir. Abdomen bölgesinde yer alan pankreas, her insanda şekil, konum ve büyüklük bakımından farklı olduğundan segmentasyonu oldukça zorlayıcıdır. Bu problemin çözümünde literatürde genellikle derin öğrenme modellerinden biri olan U-Net modeli tercih edilmektedir. Bu tez çalışmasında, pankreas segmentasyonu için Pascal üçgenindeki sayı dizilimine uygun bir mimariye sahip ve U-Net modelini temel alan yeni bir derin öğrenme modeli önerilmiştir. Önerilen bu model Pascal U-Net modeli olarak isimlendirilmiştir ve modelin başarımı iki farklı veri seti üzerinde değerlendirilmiştir. İlk olarak halka açık ve literatürde sıklıkla kullanılan bir veri seti olan The Cancer Imaging Archive Pankreas-BT veri setinden yararlanılmıştır. Ayrıca ikinci veri seti olarak Selçuk Üniversitesi Tıp Fakültesi Hastanesi Radyoloji Bölümü'nden alınan abdomen BT görüntüleri kullanılmıştır. Veri setlerindeki kayıtlardan her hasta için bir kesit görüntüsü seçilmiş ve önişleme yöntemleri uygulanarak derin öğrenme ağları için veri setleri oluşturulmuştur. Pascal U-Net modeli ile her iki veri seti üzerinde elde edilen pankreas segmentasyon sonuçlarının karşılaştırılması için, aynı veri setleri üzerinde U-Net modeli ile de segmentasyon işlemi gerçekleştirilmiştir. 2, 4 ve 6 katlı çapraz doğrulama ve 1'den 10'a kadar farklı yığın sayılarında çalıştırılan derin öğrenme modelleri sonucunda elde edilen segmentasyon haritaları, 7 farklı performans metriği kullanılarak değerlendirilmiştir. Her bir yığın sayısı ve farklı kat çapraz doğrulama ile gerçekleştirilen pankreas segmentasyonu sonuçları, 10 kez çalıştırma sonuçlarının ortalamasıdır. Hem U-Net hem de Pascal U-Net segmentasyon sonuçları 7 farklı metrik ve görsel değerlendirmeler temel alınarak analiz edilmiştir. Sonuçlar incelendiğinde; her iki veri setinde de Pascal U-Net modeli, geleneksel U-Net mimarisine karşı Dice Benzerlik Katsayısı metriği bakımından yaklaşık %1'lik bir değer ile üstünlük göstermiştir.Article Adrenal Lesion Classification on T1-Weighted Abdomen Images With Convolutional Neural Networks(2022-12-31) Solak, Ahmet; Ceylan, Rahime; Bozkurt, Mustafa Alper; Cebeci, Hakan; Koplay, MustafaAdrenal lesions are usually discovered incidentally during other health screenings and are usually benign. However, it is vital to take precautions when a malignant adrenal lesion is detected. Especially deep learning models developed in the last ten years give successful results on medical images. In this paper, adrenal lesion characterization on T1-weighted magnetic resonance abdomen images was aimed using convolutional neural network (CNN) which is one of the deep learning methods. Firstly, effects of important model parameters are assessed on performance of CNN, so optimum CNN model is obtained for classification of adrenal lesions. For a fixed number of convolution filters determined in the first stage of the study, CNN model implemented by different kernel sizes were trained. According to the best result obtained, this time the kernel size was kept constant, and experiments were made for different filter numbers. Finally, studies were carried out with CNN structures of different depths and the results were compared. As a result of the studies, when filter is selected as [5 20], the best results in the trainings conducted with a single-block CNN structure are obtained 0.97, 0.90, 0.98, 0.90, 0.90, and 0.94, for accuracy, sensitivity, specificity, precision, F1-score, and AUC score, respectively. The study was compared with the studies in the literature, and it was seen that it was superior to them.Article Citation - WoS: 9Citation - Scopus: 11Adrenal Tumor Segmentation Method for Mr Images(ELSEVIER IRELAND LTD, 2018) Barstuğan, Mücahid; Ceylan, Rahime; Asoğlu, Semih; Cebeci, Hakan; Koplay, MustafaBackground and objective: Adrenal tumors, which occur on adrenal glands, are incidentally determined. The liver, spleen, spinal cord, and kidney surround the adrenal glands. Therefore, tumors on the adrenal glands can be adherent to other organs. This is a problem in adrenal tumor segmentation. In addition, low contrast, non-standardized shape and size, homogeneity, and heterogeneity of the tumors are considered as problems in segmentation. Methods: This study proposes a computer-aided diagnosis (CAD) system to segment adrenal tumors by eliminating the above problems. The proposed hybrid method incorporates many image processing methods, which include active contour, adaptive thresholding, contrast limited adaptive histogram equalization (CLAHE), image erosion, and region growing. Results: The performance of the proposed method was assessed on 113 Magnetic Resonance (MR) images using seven metrics: sensitivity, specificity, accuracy, precision, Dice Coefficient, Jaccard Rate, and structural similarity index (SSIM). The proposed method eliminates some of the discussed problems with success rates of 74.84%, 99.99%, 99.84%, 93.49%, 82.09%, 71.24%, 99.48% for the metrics, respectively. Conclusions: This study presents a new method for adrenal tumor segmentation, and avoids some of the problems preventing accurate segmentation, especially for cyst-based tumors. (C) 2018 Elsevier B.V. All rights reserved.Master Thesis Derin Öğrenme ile Güneş Enerji Santrallerinde Üretim Tahmini(Konya Teknik Üniversitesi, 2024) Köksal, Azime İrem; Ceylan, RahimeHer geçen gün artan endüstriyel faaliyetler, sürekli artan nüfus ve teknolojik aletlerin kullanımının giderek yaygınlaşması ile enerji tüketiminde de sürekli olarak bir artış meydana gelmektedir. Hem fosil yakıtların tükenmek ile karşı karşıya oluşu hem de doğaya olumsuz etkilerinden dolayı yenilenebilir enerji kaynaklarına olan talep gün geçtikçe artmaktadır. Söz konusu bu artış ile elektrik enerjisinin üretimi ile tüketimi arasındaki dengenin sağlanmasına katkı sunmak amacı ile bu çalışma gerçekleştirilmiştir. Çalışmada güneş enerji santrallerinin üretim tahminlerinde kullanılan dört popüler derin öğrenme modeli olan Uzun Kısa Süreli Bellek (UKSB), Çok Katmanlı Algılayıcılar (ÇKA), Tekrarlayan Sinir Ağları (TSA) ve Kapı Kontrollü Tekrarlayan Birimler (KKTB) yöntemleri incelenmiş ve elde edilen sonuçlar neticesinde TSA ve KKTB modellerinin diğerlerine göre daha yüksek tahmin başarısı sergilediği tespit edilmiştir.
Research Topics
Domains
Health SciencesLife SciencesPhysical Sciences
Fields
MedicineNeuroscienceComputer ScienceHealth Professions
Subfields
Cardiology and Cardiovascular MedicineCognitive NeuroscienceRadiology, Nuclear Medicine and ImagingArtificial IntelligenceHealth Information Management
Specific Research Areas
ECG Monitoring and Analysis
EEG and Brain-Computer Interfaces
Radiomics and Machine Learning in Medical Imaging
Fuzzy Logic and Control Systems
Artificial Intelligence in Healthcare
Sustainable Development Goals
3GOOD HEALTH AND WELL-BEING
10
Research Products
16PEACE, JUSTICE AND STRONG INSTITUTIONS
1
Research Products
Documents
52
Citations
1129
h-index
13
Documents
48
Citations
844
Publication Collaboration
| Affiliation Name | Count |
|---|---|
| Selçuk University | 25 |
| Konya Technical University | 7 |
| Fatih University | 3 |
| Mehran University of Engineering and Technology | 2 |
| Erciyes University | 2 |
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Data obtained from OpenAlex
| Journal | Count |
|---|---|
| 2020 28TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) | 2 |
| 2018 2ND INTERNATIONAL SYMPOSIUM ON MULTIDISCIPLINARY STUDIES AND INNOVATIVE TECHNOLOGIES (ISMSIT) | 1 |
| 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT) | 1 |
| 2018 IEEE 13TH INTERNATIONAL SCIENTIFIC AND TECHNICAL CONFERENCE ON COMPUTER SCIENCES AND INFORMATION TECHNOLOGIES (CSIT), VOL 1 | 1 |
| 2018 International Conference on Artificial Intelligence and Data Processing (IDAP) | 1 |
Current Page: 1 / 5
Scholarly Output
33
Articles
16
Views / Downloads
96/91
Supervised MSc Theses
3
Supervised PhD Theses
3
WoS Citation Count
96
Scopus Citation Count
114
Patents
0
Projects
0
WoS Citations per Publication
2.91
Scopus Citations per Publication
3.45
Open Access Source
11
Supervised Theses
6
Scopus Quartile Distribution
Competency Cloud
