Yazar "Emiroglu, Bulent Gursel" seçeneğine göre listele

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    A comparative study for glioma classification using deep convolutional neural networks
    (Amer Inst Mathematical Sciences-Aims, 2021) Ozcan, Hakan; Emiroglu, Bulent Gursel; Sabuncuoglu, Hakan; Ozdogan, Selcuk; Soyer, Ahmet; Saygi, Tahsin
    Gliomas are a type of central nervous system (CNS) tumor that accounts for the most of malignant brain tumors. The World Health Organization (WHO) divides gliomas into four grades based on the degree of malignancy. Gliomas of grades I-II are considered low-grade gliomas (LGGs), whereas gliomas of grades III-IV are termed high-grade gliomas (HGGs). Accurate classification of HGGs and LGGs prior to malignant transformation plays a crucial role in treatment planning. Magnetic resonance imaging (MRI) is the cornerstone for glioma diagnosis. However, examination of MRI data is a time-consuming process and error prone due to human intervention. In this study we introduced a custom convolutional neural network (CNN) based deep learning model trained from scratch and compared the performance with pretrained AlexNet, GoogLeNet and SqueezeNet through transfer learning for an effective glioma grade prediction. We trained and tested the models based on pathology-proven 104 clinical cases with glioma (50 LGGs, 54 HGGs). A combination of data augmentation techniques was used to expand the training data. Five-fold cross-validation was applied to evaluate the performance of each model. We compared the models in terms of averaged values of sensitivity, specificity, F1 score, accuracy, and area under the receiver operating characteristic curve (AUC). According to the experimental results, our custom-design deep CNN model achieved comparable or even better performance than the pretrained models. Sensitivity, specificity, F1 score, accuracy and AUC values of the custom model were 0.980, 0.963, 0.970, 0.971 and 0.989, respectively. GoogLeNet showed better performance than AlexNet and SqueezeNet in terms of accuracy and AUC with a sensitivity, specificity, F1 score, accuracy, and AUC values of 0.980, 0.889, 0.933, 0.933, and 0.987, respectively. AlexNet yielded a sensitivity, specificity, F1 score, accuracy, and AUC values of 0.940, 0.907, 0.922, 0.923 and 0.970, respectively. As for SqueezeNet, the sensitivity, specificity, F1 score, accuracy, and AUC values were 0.920, 0.870, 0.893, 0.894, and 0.975, respectively. The results have shown the effectiveness and robustness of the proposed custom model in classifying gliomas into LGG and HGG. The findings suggest that the deep CNNs and transfer learning approaches can be very useful to solve classification problems in the medical domain.
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    An Application of Tree Seed Algorithm for Optimization of 50 and 100 Dimensional Numerical Functions
    (Institute of Electrical and Electronics Engineers Inc., 2021) Gungor, Imral; Emiroglu, Bulent Gursel; Uymaz, Sait Ali; Kiran, Mustafa Servet
    The Tree-Seed Algorithm is an optimization algorithm created by observing the process of growing and becoming a new tree, the seeds scattering around trees in natural life. In this study, TSA is applied to optimize high-dimensional functions. In previous studies, the performance of the tree seed algorithm applied for the optimization of low-dimensional functions has been proven. Thus, in addition to running the algorithm on 30-dimensional functions before, it has also been applied to solve 50-and 100-dimensional numerical functions. This improvement, called the tree seed algorithm, is based on the use of more solution update mechanisms instead of one mechanism. In the experiments, CEC2015 benchmarking functions are used and the developed tree seed algorithm is compared with the base state of TSA, artificial bee colony, particle swarm optimization and some variants of the differential evolution algorithm. Experimental results are obtained as mean, max, min solutions and standard deviation of 30 different runs. As a result, it is observed by the studies that the developed algorithm gives successful results. © 2021 IEEE.
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    Öğe
    Integration search strategies in tree seed algorithm for high dimensional function optimization
    (SPRINGER HEIDELBERG, 2020) Gungor, Imral; Emiroglu, Bulent Gursel; Cinar, Ahmet Cevahir; Kiran, Mustafa Servet
    The tree-seed algorithm, TSA for short, is a new population-based intelligent optimization algorithm developed for solving continuous optimization problems by inspiring the relationship between trees and their seeds. The locations of trees and seeds correspond to the possible solutions of the optimization problem on the search space. By using this model, the continuous optimization problems with lower dimensions are solved effectively, but its performance dramatically decreases on solving higher dimensional optimization problems. In order to address this issue in the basic TSA, an integration of different solution update rules are proposed in this study for solving high dimensional continuous optimization problems. Based on the search tendency parameter, which is a peculiar control parameter of TSA, five update rules and a withering process are utilized for obtaining seeds for the trees. The performance of the proposed method is investigated on basic 30-dimensional twelve numerical benchmark functions and CEC (congress on evolutionary computation) 2015 test suite. The performance of the proposed approach is also compared with the artificial bee colony algorithm, particle swarm optimization algorithm, genetic algorithm, pure random search algorithm and differential evolution variants. Experimental comparisons show that the proposed method is better than the basic method in terms of solution quality, robustness and convergence characteristics.
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    Leveraging Deep Learning for Enhanced Detection of Alzheimer's Disease Through Morphometric Analysis of Brain Images
    (Int Information & Engineering Technology Assoc, 2023) Celebi, Selahattin Baris; Emiroglu, Bulent Gursel
    This study investigates the efficacy of tensor-based morphometry (TBM) in detecting Alzheimer's Disease (AD) using deep learning techniques. The primary focus is on discerning the volumetric variations in brain tissues characteristic of AD, Mild Cognitive Impairment (MCI), and cognitively normal (CN) conditions. TBM, as a measure of minute local volume differences, is employed as the distinguishing feature. The results are juxtaposed with those obtained from machine- learning-based methods, trained using a variety of medical images. Three unique models were developed for this purpose. The first model, trained using medial slices of the brain (train: 1622; test: 406), displayed an accuracy of less than 50%. The second model utilized axial brain slices procured at 5-pixel intervals, encompassing the hippocampus and the temporal lobe (train: 1632; test: 406), and demonstrated a significantly improved accuracy of 93%. The third model, fine-tuned with small kernel sizes to better extract localized changes from the image data used in the second model, achieved an accuracy of 92%. The findings suggest that the application of TBM and deep learning to medial slices alone is insufficient for an accurate diagnosis of AD. However, employing TBM with deep learning techniques to slices covering the hippocampus and temporal lobe can potentially offer a highly accurate approach for early AD detection. Notably, the use of small filters to extract detailed features from TBM did not enhance the model's performance. This research underscores the potential of deep learning in advancing the field of AD detection and diagnosis, providing crucial insights into the future development of diagnostic tools.
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    Predicting 1p/19q chromosomal deletion of brain tumors using machine learning
    (Ice Publishing, 2021) Cinarer, Gokalp; Emiroglu, Bulent Gursel; Yurttakal, Ahmet Hasim
    Advances in molecular and genetic technologies have enabled the study of mutation and molecular changes in gliomas. The 1p/19q coding state of gliomas is important in predicting pathogenesis-based pharmacological treatments and determining innovative immunotherapeutic strategies. In this study, T1-weighted and T2-weighted fluid-attenuated inversion recovery magnetic resonance imaging (MRI) images of 121 low-grade glioma patients with biopsy-proven 1p/19q coding status and no deletion (n = 40) or co-deletion (n = 81) were used. First, regions of interests were segmented with the grow-cut algorithm. Later, 851 radiomic features including three-dimensional wavelet preprocessed and non-preprocessed ones were extracted from six different matrices such as first order, shape and texture. The extracted features were preprocessed with the synthetic minority over-sampling technique algorithm. Next, the 1p/19q decoding states of gliomas were classified using machine-learning algorithms. The best classification in the classification of glioma grades (grade II and grade III) according to 1p/19q coding status was obtained by using the logistic regression algorithm, with 93.94% accuracy and 94.74% area under the curve values. In conclusion, it was determined that non-invasive estimation of 1p/19q status from MRI images enables the selection of effective treatment strategies with early diagnosis using machine-learning algorithms without the need for surgical biopsy.
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    Use of Augmented Reality in Mobile Devices for Educational Purposes
    (Igi Global, 2017) Emiroglu, Bulent Gursel; Kurt, Adile Askim
    Use of technology in education has been widespread in the last decade, thanks to developments and improvements in information and communication technologies, especially in mobile devices. Among the fields in which mobile devices play important roles, education is one of the leading ones. Mobile devices help teachers and learners access educational resources when needed. To increase the reality of virtual learning environments on mobile devices, Augmented Reality (AR) technologies were introduced for mobile platforms, and the term Mobile Augmented Reality (MAR) arose. MAR opens a new door for educators and trainers to experience new methods of teaching for mobile learners. In this chapter, educational use of AR on mobile devices will be explained. Throughout the content of the chapter, readers will be informed about how AR applications changed people's teaching and learning styles.