Yazar "Temiz, Hakan" seçeneğine göre listele

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    Classification of medical imaging technologies: Results from Türkiye
    (BioMed Central Ltd, 2025) Temiz, Hakan; Kara, Tuncay
    Background: Regional disparities in access to medical diagnostic imaging technologies (MDITs) present a significant barrier to achieving health equity, particularly in developing countries. Understanding how these technologies are distributed and utilized is essential for informing equitable health policy. Method: This study examines the distribution and utilization of MDITs across Türkiye’s 12 NUTS regions using a hierarchical clustering approach. Unlike previous studies, the analysis incorporates both technological capacity and utilization (CaU) variables, evaluated jointly and independently. Imaging modalities are also stratified based on their technological complexity and investment requirements to capture nuanced regional patterns. Results: Findings indicate that although Türkiye demonstrates an overall balanced distribution of MDITs, notable regional disparities in utilization efficiency remain. Regions exhibiting similar usage patterns tend to cluster together irrespective of geographic proximity. Interestingly, the clusters often transcend geographical proximity; regions located at opposite ends of the country tend to cluster on the basis of similar utilization patterns. This may suggest that disparities between administrative centers and rural areas are less pronounced than previously assumed. These patterns imply that institutional capacity, healthcare workforce distribution, and demographic demand may have a stronger influence on utilization than spatial location. Conclusion: The study highlights a disconnect between capacity and actual use of diagnostic imaging technologies, underscoring the need for targeted policy interventions. It also suggests that regional utilization patterns may align more with functional similarities than with geographic proximity. Moreover, analyzing technological capacity and utilization variables separately—rather than as a combined index—yielded more transparent and objective insights into regional disparities. These findings contribute to optimizing health resource allocation and support evidence-based policymaking aimed at advancing equitable access to diagnostic services, aligning with Türkiye’s commitment to universal health coverage.
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    A comparative study on super resolution with deep learning
    (IEEE, 2018) Temiz, Hakan; Tüfekçi, Aslıhan; Bilge, Hasan Şakir
    Deep learning architectures are applied in the solution of many problems and give very successful results compared to other methods. One of these problems is the Super Resolution problem. In this study, we tried to solve the problem of super resolution by using different deep learning architectures to obtain higher resolution images. The models used in this study are focused on the images scaled up by factors of 2, 3 and 4. As a result of the experimental studies, the model success is increased as the network depth and samples are increased. Instead of a shallow model with more number of parameters, a deep model with lower number of parameters offers more successful results.
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    DeepSR: a deep learning tool for image super resolution
    (ELSEVIER, 2023) Temiz, Hakan
    An open source tool is introduced that provides a versatile environment to meet the needs of re- searchers in developing deep learning (DL) algorithms for single image super-resolution reconstruction (SISR). The processes of SISR were carefully studied, unified and integrated to create software that can be used by the community for any type of imaging method such as aerial, medical, optical, etc. DeepSR allows easy implementation of SISR application with rapidly prototyped DL models, and detailed reporting and recording of the results. The entire experiment can be done with simple command line scripts. It can be easily extended by user-defined metrics, augmentations, callbacks, etc.
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    Enhancing the resolution of historical Ottoman texts using deep learning-based super-resolution techniques
    (International Information and Engineering Technology Association, 2023) Temiz, Hakan
    The Ottoman Empire's extensive archives hold valuable insights into centuries of history, necessitating the preservation and transfer of this rich heritage to future generations. To facilitate access and analysis, numerous digitization efforts have been undertaken to transform these valuable resources into digital formats. The quality of digitized documents directly impacts the success of tasks such as text search, analysis, and character recognition. This study aims to enhance the resolution and overall image quality of Ottoman archive text images using four deep learning-based super-resolution (SR) algorithms: VDSR, SRCNN, DECUSR, and RED-Net. The performance of these algorithms was assessed using SSIM, PSNR, SCC, and VIF image quality measures (IQMs) and evaluated in terms of human visual system perception. All SR algorithms achieved promising IQM scores and a significant improvement in image quality. Experimental results demonstrate the potential of deep learning-based SR techniques in enhancing the resolution of historical Ottoman text images, paving the way for more accurate character recognition, text processing, and analysis of archival documents.
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    An experimental study on hyper parameters for training deep convolutional networks
    (Institute of Electrical and Electronics Engineers Inc., 2020) Temiz, Hakan
    When training deep networks, it is crucial to obtain a network that offers optimum performance by trying different values of many hyper parameters and combinations of these values. Theoretically, the optimal set of values, which ensure the maximum performance of the network, can be found by giving these parameters numerous different values. However, it is not feasible to try all combinations of values. The a priori information regarding the contribution levels of hyper parameters and their values to the performance of the network will narrow the search space and enable researchers to easily and quickly obtain the network with optimum performance. In this study, a priori information is investigated that will guide in searching for most important hyper parameters and their ideal values that ensure optimum performance of a typical convolutional neural network in single image super resolution. For this purpose, the importance levels of the 5 most commonly used hyper parameters in training, and their optimum values were investigated. By giving two different values that are widely used or known to give good results from previous works in the literature for each hyper parameter, in total, 32 different training procedure were performed. The results showed that the learning rate has the most important effect on the performance of the network, then normalization, and then the size of the input image given to the model during training. It has also been found that the batch number and step count parameter values do not make a significant change in the performance of the network. The results obtained from this study could help researchers in determining the training parameters and their values in order to efficiently and rapidly obtain optimum network performance
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    SI and binary prefixes: Clearing the confusion
    (Association for Computing Machinery, 2023) Temiz, Hakan
    THE INTERNATIONAL SYSTEM of Units (SI), organized by the International Bureau of Weights and Measures (BIPM), defines several prefixes to denote the quantities between 10–24 and 10+24, given in Table 1. It allows expression of very large or small quantities of all units of measurement in common scientific notation accepted by the international community. For example, the prefixes kilo (k), mega (M), giga (G), and tera (T) indicate 103n, for n= 1, 2, 3, and 4, respectively. However, when expressing data quantities and memory addresses, SI prefixes (SIPs) are misused to indicate binary multiples. In this non-standard adoption, for a number n, where n=1, 2, …, and 8, each prefix specifies 210n instead of their original values (103n). Almost everyone, including scientists and engineers, thinks that this non-standard acceptance is correct. On the other hand, the prefixes for binary multiples were already defined by the relevant standards approximately 23 years ago. This nonstandard notation confuses at the very least or can cause rather serious problems. Hence, all parties should immediately abandon this misconception and disseminate the correct information to everyone.
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    Super resolution of B-mode ultrasound images with deep learning
    (Institute of Electrical and Electronics Engineers Inc., 2020) Temiz, Hakan; Bilge, Hasan Şakir
    Ultrasound offers a safe, non-invasive, and inexpensive way of imaging. However, due to its natural intrinsic imaging characteristics, it produces poor quality images with low resolution (LR) compared to other medical imaging modalities. Various image enhancement techniques have been extensively studied to overcome these shortcomings. Super-resolution (SR) is one of these methods, which endeavor to obtain high resolution (HR) images from LR images while enlarging them. Numerous studies have already utilized different SR techniques in various stages of ultrasound imaging (USI). Unlike other studies, which aimed at obtaining SR in the pre-processing phase or early stages of the post-processing phase of USI, we achieved SR on B-mode ultrasound images, which is the last stage of USI. We constructed a deep convolutional neural network (CNN) and trained it with a very large dataset of B-mode ultrasound images for the scale factors 2, 3, 4, and 8. We evaluated the performance of our proposed model quantitatively with eight image quality measures. The quantitative results revealed that our algorithm is much more successful than other methods at each magnification factor. Furthermore, we also verified that there is a statistically significant difference between our approach and others. Besides, qualitative analysis of the reconstructed images also confirms that it produces much better quality HR images than other methods in terms of the human visual system.