Yazar "Emiroğlu, Bülent Gürsel" seçeneğine göre listele

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    A Novel Deep Dense Block-Based Model for Detecting Alzheimer’s Disease
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023) Çelebi, Selahattin Barış; Emiroğlu, Bülent Gürsel
    Alzheimer’s disease (AD), the most common form of dementia and neurological disorder, affects a significant number of elderly people worldwide. The main objective of this study was to develop an effective method for quickly diagnosing healthy individuals (CN) before they progress to mild cognitive impairment (MCI). Moreover, this study presents a unique approach to decomposing AD into stages using machine-learning architectures with the help of tensor-based morphometric image analysis. The proposed model, which uses a neural network built on the Xception architecture, was thoroughly assessed by comparing it with the most recent convolutional neural network (CNN) models described in the literature. The proposed method outperformed the other models in terms of performance, achieving an impressive average classification accuracy of 95.81% using the dataset. It also had very high sensitivity, specificity, accuracy, and F1 scores, with average scores of 95.41%, 97.92%, 95.01%, and 95.21%, respectively. In addition, it showed a superior classification ability compared to alternative methods, especially for MCI estimation, as evidenced by a mean area under the ROC curve (AUC) of 0.97. Our study demonstrated the effectiveness of deep-learning-based morphometric analysis using brain images for early AD diagnosis. © 2023 by the authors.
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    Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz
    (2023) Çelebi, Selahattin Barış; Emiroğlu, Bülent Gürsel
    Alzheimer, dünyadaki en yaygın bunama türüdür ve şu an için kullanılan tedavi yöntemleri sadece hastalığın ilerleyişini önleme amacına yöneliktir. Beyin dokusu hacmi Alzheimer hastalığı (AD) nedeniyle değişir. Tensör tabanlı morfometri (TBM) yardımıyla, hastalığın beyin dokularında neden olduğu değişiklikler izlenebilir. Bu çalışmada AD hastaları ve Bilişsel Normal(ler) (CN'ler) grubu denekleri arasında ayrım yapmak için etkili bir yöntem geliştirmek amaçlanmıştır. TBM veya küçük yerel hacim farklılıkları, sınıflandırma özelliği olarak benimsenmiştir. AD/CN sınıfına ait 3D TBM morfometrik görüntülerinden hipokampus ve temporal lobu kapsayan 5 piksel aralıklı eksenel beyin görüntü dilimleri 2D olarak kaydedildi. Daha sonra her bir klinik gruptan (AD; CN) elde edilen veri setinin %60'ı eğitim, %20’si validasyon ve %20’si test veri setleri olarak ayrıldı (Eğitim: 480; doğrulama: 120; test: 120). Model validasyon (%92.5) ve test (%89) doğruluk değerleri ile AD/CN tahmini gerçekleştirdi. Sonuçlar, Derin öğrenme ile hipokampus ve temporal lobu kapsayan dilimlerden elde edilen TBM'nin AD'nin tanısında yüksek doğrulukla uygulanabileceğini göstermektedir.
  • Küçük Resim
    Öğe
    Bulut Tabanlı Öğrenme Yönetim Sistemi Seçiminde Bulanık Çok Kriterli Karar Analizi Yaklaşımı
    (2020) Özcan, Hakan; Emiroğlu, Bülent Gürsel
    Bulut bilişim teknolojisinin gelişmesiyle öğrenme yönetim sistemleri (ÖYS’ler) yeni özellikler ve servis seçenekleri kazanmıştır. Buna bağlı olarak artan ürün alternatifleri arasından seçim yapma süreci zorlaşmıştır. Belli kriterlere bağlı en uygun bulut tabanlı ÖYS’yi seçmek kurumlar için önemli bir karar verme sorunu olmuştur. Bu çalışmada, kurumların bir grup bulut tabanlı ÖYS arasından belli kriterlere uygun seçim yapabilmesini kolaylaştıracak Bulanık Analitik Hiyerarşi Süreci (BAHS) tabanlı bir model geliştirilmiştir. Bu modelde, bulut tabanlı ÖYS seçiminde etkili olabilecek içerik desteği, etkileşim ve iş birliği, ölçme ve değerlendirme, ders yapısı, arayüz, verimlilik araçları, platform esnekliği, ölçeklenebilirlik, güvenlik, destek ve lisanslama kriterleri literatüre ve uzman görüşlerine dayalı incelenmiş ve oluşturulan bir hiyerarşik yapı ile sunulmuştur. Çalışmada hem kriterler hem de durum çalışması kapsamında ele alınan altı alternatif, çevrim-içi eğitim alanında uzman yedi karar verici tarafından değerlendirilmiştir. Belirlenen kriterlere bağlı olarak, seçilen alternatifler arasında yapılan bulanık ikili karşılaştırmalar sonucu en uygun bulut tabanlı ÖYS, TalentLMS olarak belirlenmiştir.
  • Küçük Resim
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    Investigating Faculty Members’ Perceived Usability of Edmodo Learning Management System
    (2019) Emiroğlu, Bülent Gürsel
    New developments in the field of computer science have allowed thedevelopment and dissemination of the systems that universities use tosupport their educational processes. Among these systems, learningmanagement systems are at the top of the tools faculty members use tocarry out online learning processes. Today, with the increase in learningmanagement systems, meeting the need for usability testing is becomingmore important both for ensuring user satisfaction and for revealingappropriate software solutions. This study seeks to understand how facultymembers perceive the usability of a cloud-based learning managementsystem that is widely used in many universities. The study adopted theconvenience sampling method of purposeful sampling strategies. In thequantitative part of the study, the perceived usability was examined withthe System Usability Scale by 12 faculty members. In the qualitative partof the study, the data about the usability issues that faculty members facein the software were collected by semi-structured interviews andinvestigated using content analysis method. Findings have shown that theweb application side of the learning management system got an aboveaverage perceived usability. Besides, some issues that affect perceivedusability were found regarding to system loading speed, site structure,assignment management and grading mechanism, Turkish languagesupport, content filtering and labeling, and finally the badge system. Thefindings of this study are intended to provide guidance to softwaredevelopers and decision makers to better understand the perceivedusability of faculty members and to identify the need for improvement of thesoftware usability.
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    Leveraging Deep Learning for Enhanced Detection of Alzheimer’s Disease Through Morphometric Analysis of Brain Images
    (International Information and Engineering Technology Association, 2023) Çelebi, Selahattin Barış; Emiroğlu, Bülent Gürsel
    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. © 2023 Lavoisier. All rights reserved.
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    Prediction of Glioma Grades Using Deep Learning with Wavelet Radiomic Features
    (MDPI, 2020) Çınarer, Gökalp; Emiroğlu, Bülent Gürsel; Yurttakal, Ahmet Haşim
    Gliomas are the most common primary brain tumors. They are classified into 4 grades (Grade I-II-III-IV) according to the guidelines of the World Health Organization (WHO). The accurate grading of gliomas has clinical significance for planning prognostic treatments, pre-diagnosis, monitoring and administration of chemotherapy. The purpose of this study is to develop a deep learning-based classification method using radiomic features of brain tumor glioma grades with deep neural network (DNN). The classifier was combined with the discrete wavelet transform (DWT) the powerful feature extraction tool. This study primarily focuses on the four main aspects of the radiomic workflow, namely tumor segmentation, feature extraction, analysis, and classification. We evaluated data from 121 patients with brain tumors (Grade II,n= 77; Grade III,n= 44) from The Cancer Imaging Archive, and 744 radiomic features were obtained by applying low sub-band and high sub-band 3D wavelet transform filters to the 3D tumor images. Quantitative values were statistically analyzed with MannWhitney U tests and 126 radiomic features with significant statistical properties were selected in eight different wavelet filters. Classification performances of 3D wavelet transform filter groups were measured using accuracy, sensitivity, F1 score, and specificity values using the deep learning classifier model. The proposed model was highly effective in grading gliomas with 96.15% accuracy, 94.12% precision, 100% recall, 96.97% F1 score, and 98.75% Area under the ROC curve. As a result, deep learning and feature selection techniques with wavelet transform filters can be accurately applied using the proposed method in glioma grade classification.
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    Private school teachers’ views on using tablets in education
    (Ankara University, 2016) Emiroğlu, Bülent Gürsel
    In this study, views of the 257 teachers, working at the Bahçeşehir Colleges located in Ankara, Kocaeli, Kayseri, Adana, Tokat, İzmir, Burhaniye and Edirne, about the use of tablet computers in education are tried to be identified. In the study, designed as a general survey model, data were collected with a questionnaire form composed of open-ended questions. At the questionnaire form, open ended questions were used to discover which applications were used by the teachers, their educational expectations from those applications, and if necessary training and required conditions about the educational activities on tablet computers are provided, whether they want to develop their own applications and/or course content on tablet computers or not. At the end of the study it was found that; the most popular applications used by the teachers on tablet computers were Keynote and iMovie, least popular was Human Organs, highly ranked expectations from the educational applications used were gaining students’ attention and active participation to the lesson, and low ranked were querying of students what they learned and discovery learning. Another result gained from the study was that the most demanded activity of teachers on tablet computers was writing letters, syllables and words, and least demanded was physical activities. Furthermore, the results of the study also showed that quizzes were the mostly intended activity on the tablet computers by teachers and followed by crosswords. In the study, another result was that most of the teachers requested proper training for themselves, however, only very few of them did not request for that. © 2016, Ankara University. All rights reserved.