Yazar "Okut, Hayrettin" seçeneğine göre listele

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    Investigation of genetic structure in van cats using microsatellite markers
    (Elsevier Science Bv, 2017) Koyun, Hasan; Koncagul, Seyrani; Karakus, Kadir; Okut, Hayrettin; Kucuk, Mursel; Yilmaz, Ayhan; Ozkan, Cumali
    [Abstract Not Available]
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    Öğe
    Paradigm shift from Artificial Neural Networks (ANNs) to deep Convolutional Neural Networks (DCNNs) in the field of medical image processing
    (Pergamon-Elsevier Science Ltd, 2024) Abut, Serdar; Okut, Hayrettin; Kallail, K. James
    Images and other types of unstructural data in the medical domain are rapidly becoming data-intensive. Actionable insights from these complex data present new opportunities but also pose new challenges for classification or segmentation of unstructural data sources. Over the years, medical problems have been solved by combining traditional statistical methods with image processing methods. Both the increase in the size of the data and the increase in the resolution are among the factors that shape the ongoing improvements in artificial intelligence (AI), particularly concerning deep learning (DL) techniques for evaluation of these medical data to identify, classify, and quantify patterns for clinical needs. At this point, it is important to understand how Artificial Neural Networks (ANNs), which are an important milestone in interpreting big data, transform into Deep Convolutional Neural Networks (DCNNs) and to predict where the change will go. We aimed to explain the needs of these stages in medical image processing through the studies in the literature. At the same time, information is provided about the studies that lead to paradigm shift and try to solve the image related medical problems by using DCNNs. With the increase in the knowledge of medical doctors on this subject, it will be possible to look at the solution of new problems in computer science from different perspectives.
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    Öğe
    The Importance of artificial neural networks in decision making for the field of medicine
    (Nova Science Publishers, Inc., 2024) Abut, Serdar; Okut, Hayrettin
    This book chapter explores the integration of machine learning techniques, particularly deep neural networks, in the field of medical image processing for precision medicine. The healthcare industry has accumulated vast amounts of complex data, and advancements in technology have led to an increase in structured and unstructured medical data. The chapter discusses the historical development of image processing techniques, moving from labor-intensive approaches to more efficient and faster operations using artificial neural networks. Various feature extraction methods, with a focus on dimensionality reduction, are investigated to optimize the performance of neural networks. The application of deep neural network models in medical imaging is explored, with a gradual implementation strategy proposed to address challenges related to data variability across institutions. The potential benefits and obstacles of using deep neural network models for psychiatric diagnoses and neonatal early detection are discussed. Throughout the chapter, the importance of effective communication between data scientists, software engineers, and clinicians is emphasized for the development of robust and practical artificial intelligence systems in healthcare. The overall viewpoint highlight how technological advancements in image processing have transformed artificial intelligence-based applications in the medical field and future developments can be looked at in a different light in this area. © 2024 by Nova Science Publishers, Inc. All rights reserved.