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    A new 3D MRI segmentation method based on Generative Adversarial Network and Atrous Convolution
    (Elsevier Sci Ltd, 2022) Celik, Gaffari; Talu, Muhammed Fatih
    Background and aim: Although many algorithms have been proposed to segment brain structures in MRI scans, comparison of different algorithms in the same data set is rarely done. Many methods still run on privately held data and include comparisons with previous versions. The purpose of this study is to introduce a new Generative Adversarial Network (GAN) based segmentation architecture. Brain tissues on 3D-MRI scans with T1w modality were segmented into three (GM, WM, CSF) and eight (CGM, BG, WM, WMH, CF, VE, CE ve BS) parts. Methods: The proposed approach (Vol2SegGAN) consists of two parts: preprocessing and segmentation. The preprocessing includes extraction of the brain region, editing of labels in the datasets, MNI152 registration, clipping/sampling. The segmentation part is carried out by the collaboration of a generator (includes ACFP and PAM modules) and a discriminator (distinguishes real/fake) architectures. Conclusions: In the three part segmentation process, the proposed method showed the best segmentation success in CSF (Dice = 0.739, VS = 0.967), GM (Dice = 0.878, HD = 2.378) and WM (HD = 2.105) tissues, and the second best segmentation success in WM (Dice = 0.793, VS = 0.972) tissue according to Dice and VS metrics. Similarly, in the segmentation of eight parts, it is seen that it has the best success according to VS and HD metrics and the second best according to Dice metric. Vol2SegGAN, which has fewer parameters (6,883 mil.) than existing architectures, has an average of 11-12 s (CPU) or 0-1 s (GPU) to segment a sample 3D-MRI. Implementation codes of the proposed architecture are available on the github page1.
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    Resizing and cleaning of histopathological images using generative adversarial networks
    (Elsevier, 2020) Celik, Gaffari; Talu, Muhammed Fatih
    Bilinear and Bicubic interpolation techniques are frequently used to increase image resolution. These techniques with data modeling approach are replaced by intelligent systems that can learn automatically from data. SRGAN is a modern Generative Adversarial Network developed as an alternative to classical interpolation techniques. His ability to produce images in super resolution has attracted the attention of many researchers. In this study, noise elimination performance of super resolution generative adversarial network (SRGAN) with image magnification was investigated. The results of the noise cleaning were compared with the classical approaches (mean, median, adaptive filters). SSIM, PSNR and FFT_MSE metrics were evaluated in experimental studies using images in the data set Camelyon17. When the results were evaluated, it was observed that SRGAN was superior to the classical approaches not only in increasing the resolution but also in the noise cleaning area. (C) 2019 Published by Elsevier B.V.
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    Spiking Neural Network Applications
    (Ieee, 2017) Celik, Gaffari; Talu, M. Fatih
    Spiking Neural Network (SNN) are 3rd Generation Artificial Neural Networks (ANN) models. The fact that time information is processed in the form of spikes and there are multiple synapses between cells (neurons) are the most important features that distinguish SNN from previous generations. In this study, artificial learning systems which can learn by using basic logical operators such as AND, OR, XOR have been developed in order to understand SNN structure. In SNN, we tried to find optimal values for these parameters by examining the effect of the number of connections between cells and delays between connections to learning success.
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    Vortex Optimization Algorithm Based Fabric Defect Detection
    (Ieee, 2018) Yazan, Ersan; Celik, Gaffari; Talu, Muhammed Fatih; Yeroglu, Celaleddin
    In textile industry, defects that occur in fabrics during production processes cause the producers to suffer large losses of money. Various studies have been carried out to minimize these losses. There are two types of defect detection methods, that are human-focused and machine-focused defect defect detection. In human-focused systems, defect detection is performed after the production phase. This does not provide an advantage for the manufacturer. Defect detection with machine-focused systems have better results. In this study widely used machine-oriented fabric defect detection approaches have been analyzed and a method is proposed based on using Fourier transform with bandpass filter. The Vortex Optimization Algorithm (GOA) is used to obtain the most suitable parameters of the bandpass filter better.