Browsing by Author "Karayegen, Gokay"

Now showing 1 - 4 of 4

Aligned Polyvinylpyrrolidone Nanofibers with Advanced Electrospinning for Biomedical Applications
(2018) Karayegen, Gokay; Kocum, I. Cengiz; Serdaroglu, Dilek Cokeliler; Dogan, Mustafa; 0000-0001-5215-8887; 30400080; I-4296-2019
BACKGROUND: Electrospinning is a highly effective method in order to generate nano-scaled fibers. In conventional electrospinning technique, geometry of nanofibers are mostly random due to the chaotic behavior of polymer jet. OBJECTIVE: Purpose of this study is to produce aligned nanofibers from PVP polymers with advanced electrospinning technique in order to be used in a potential novel sensor applications, tissue regeneration and engineering. METHODS: In this study, by using finite hollow cylinder focusing electrodes, an external electrostatic field is created. With these electrodes, it is aimed to decrease whipping instability of polymer jet. In addition, it is also investigated that the alignment ratio of nanofibers by using conductive parallel electrodes which placed through jet trajectory. RESULTS: In conclusion, with the effect of electrical field created by cylinder electrodes, radius of the fiber dispersion on the collector was able to be reduced and aligned nanofibers were successfully produced by using electrical field generated from the parallel plates. CONCLUSIONS: Radius of the fiber dispersion on the collector is 9.95 mm and fiber diameters varied between 800 nm and 3 mu m. Additionally, alignment ratio of the fibers is determined with ImageJ software. These alignment of nanofibers can be used in tissue engineering applications and sensor applications.
Brain tumor prediction on MR images with semantic segmentation by using deep learning network and 3D imaging of tumor region
(2021) Karayegen, Gokay; Aksahin, Mehmet Feyzi; 0000-0001-6294-9279
When it comes to medical image segmentation on brain MR images, using deep learning techniques has a significant impact to predict tumor existence. Manual segmentation of a brain tumor is a time-consuming task and depends on knowledge and experience of physicians. In this paper, we present a semantic segmentation method by utilizing convolutional neural network to automatically segment brain tumor on 3D Brain Tumor Segmentation (BraTS) image data sets that comprise four different imaging modalities (T1, T1C, T2 and Flair). In addition, our study includes 3D imaging of whole brain and comparison between ground truth and predicted labels in 3D. In order to obtain exact tumor region and dimensions such as height, width and depth, this method was successfully applied and images were displayed different planes including sagittal, coronal and axial. Evaluation results of semantic segmentation which was executed by a deep learning network are significantly promising in terms of tumor prediction. Mean prediction ratio was determined as 91.718. Mean IoU (Intersection over Union) and Mean BF score were calculated as 86.946 and 92.938, respectively. Finally, dice scores of the test images were showed significant similarity between ground truth and predicted labels. As a result, both semantic segmentation metrics and 3D imaging can be interpreted as meaningful for diagnosing brain tumor accurately.
Brain Tumor Prediction with Deep Learning and Tumor Volume Calculation
(2021) Karayegen, Gokay; Aksahin, Mehmet Feyzi
Comprehensive Data Analysis Of White Blood Cells With Classification And Segmentation By Using Deep Learning Approaches
(Başkent Üniversitesi Mühendislik Fakültesi, 2024-04-05) Ozcan, Seyma Nur; Uyar, Tansel; Karayegen, Gokay
Deep learning approaches have frequently been used in the classification and segmentation of human peripheral blood cells. The common feature of previous studies was that they used more than one dataset, but used them separately. No study has been found that combines more than two datasets to use together. In classification, five types of white blood cells were identified by using a mixture of four different datasets. In segmentation, four types of white blood cells were determined, and three different neural networks, including CNN (Convolutional Neural Network), UNet and SegNet, were applied. The classification results of the presented study were compared with those of related studies. The balanced accuracy was 98.03%, and the test accuracy of the train-independent dataset was determined to be 97.27%. For segmentation, accuracy rates of 98.9% for train-dependent dataset and 92.82% for train-independent dataset for the proposed CNN were obtained in both nucleus and cytoplasm detection. In the presented study, the proposed method showed that it could detect white blood cells from a train-independent dataset with high accuracy. Additionally, it is promising as a diagnostic tool that can be used in the clinical field, with successful results in classification and segmentation.