Özkan, Akın
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Ö.,Akın
Özkan,A.
Akin, Ozkan
Akın, Özkan
A.,Özkan
Ozkan,A.
A.,Ozkan
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A., Ozkan
Özkan, Akın
Ozkan, Akin
Özkan,A.
Akin, Ozkan
Akın, Özkan
A.,Özkan
Ozkan,A.
A.,Ozkan
O.,Akin
O., Akin
A., Ozkan
Özkan, Akın
Ozkan, Akin
Job Title
Araştırma Görevlisi
Email Address
akin.ozkan@atilim.edu.tr
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Scholarly Output
8
Articles
3
Citation Count
33
Supervised Theses
2
8 results
Scholarly Output Search Results
Now showing 1 - 8 of 8
Article Citation Count: 0Selective word encoding for effective text representation(Tubitak Scientific & Technological Research Council Turkey, 2019) Özkan, Akın; Özkan, Akın; Department of Electrical & Electronics EngineeringDetermining the category of a text document from its semantic content is highly motivated in the literatureand it has been extensively studied in various applications. Also, the compact representation of the text is a fundamental step in achieving precise results for the applications and the studies are generously concentrated to improve itsperformance. In particular, the studies which exploit the aggregation of word-level representations are the mainstreamtechniques used in the problem. In this paper, we tackle text representation to achieve high performance in differenttext classification tasks. Throughout the paper, three critical contributions are presented. First, to encode the wordlevel representations for each text, we adapt a trainable orderless aggregation algorithm to obtain a more discriminativeabstract representation by transforming word vectors to the text-level representation. Second, we propose an effectiveterm-weighting scheme to compute the relative importance of words from the context based on their conjunction with theproblem in an end-to-end learning manner. Third, we present a weighted loss function to mitigate the class-imbalanceproblem between the categories. To evaluate the performance, we collect two distinct datasets as Turkish parliamentrecords (i.e. written speeches of four major political parties including 30731/7683 train and test documents) and newspaper articles (i.e. daily articles of the columnists including 16000/3200 train and test documents) whose data is availableon the web. From the results, the proposed method introduces significant performance improvements to the baselinetechniques (i.e. VLAD and Fisher Vector) and achieves 0.823% and 0.878% true prediction accuracies for the partymembership and the estimation of the category of articles respectively. The performance validates that the proposed contributions (i.e. trainable word-encoding model, trainable term-weighting scheme and weighted loss function) significantlyoutperform the baselines.Conference Object Citation Count: 1Method proposal for distinction of microscope objectives on hemocytometer images;(Institute of Electrical and Electronics Engineers Inc., 2016) Özkan, Akın; Isgor,S.B.; İşgör, Sultan Belgin; Şengül, Gökhan; Department of Electrical & Electronics Engineering; Chemical Engineering; Computer EngineeringHemocytometer is a special glass plate apparatus used for cell counting that has straight lines (counting chamber) in certain size. Leveraging this special lam and microscope, a cell concentration on an available cell suspension can be estimated. The automation process of hemocytometer images will assist several research disciplines to improve consistency of results and to reduce human labor. Different objective measurements can be utilized to analyze a cell sample on microscope. These differences affect the detail of image content. Basically, while the objective value is getting increased, image scale and detail level taken from image will increase, yet visible area becomes narrower. Due to this variation, different self-cell counting approaches should be developed for images taken with different objective values. In this paper, using the hemocytometer images gathered from a microscope, a novel approach is introduced for which can estimate objective values of a microscope with machine learning methods automatically. For this purpose, a frequency-based visual feature is proposed which embraces hemocytometer structure well. As a result of the conducted tests, %100 distinction accuracy is achieved with the proposed method. © 2016 IEEE.Doctoral Thesis Bilgisayarlı görme ve makine öğrenme'ye dayalı olarak trapan mavisi boya dışlama tabanlı ışık mikroskoplarının otomatize hücre sayarına uyarlanabilir dönüşüm yöntemi(2017) Özkan, Akın; İşgör, Sultan Belgin; İşgör, Sultan Belgin; Şengül, Gökhan; Department of Electrical & Electronics Engineering; Chemical EngineeringHücre biyolojisi deneylerinin hemen hemen hepsi, hücre çoğalmasını ve yaşayabilirliğini izlemek için düzenli olarak hücrelerin sayımını içerir. Hücrenin miktarı ve kalitesinin bilgisi, deneysel standardizasyon ve toksisite etkisi tahmini için önemli parametrelerdir. Hücreleri saymak için hemositometre tabanlı elle sayma ve otomatik hücre sayacının kullanımı gibi iki farklı yaklaşım vardır. Yöntemlerden her ikisinin de avantajları ve dezavantajları vardır. Yüksek yatırım ve operasyonel maliyet otomatik hücre sayaçlarının geniş kullanımını sınırlar. Öte yandan, hemositometreye dayalı manuel hücre sayımı, hücre sayımının güvenilirliğinin, operatörün deneyimine ve yorgunluğuna büyük ölçüde bağlı olduğu gerçeği ile çeşitli sınırlamaları vardır. . Uzun zaman gereksinimi ve insan işgücü elle işleme sürecinin iki dezavantajı olarak sayılabilir. Bu tez, görüntü işleme ve makine öğrenmeyi esas alan dönüştürme metodolojisini tanımlayarak hücre sayımı için en gelişmiş alternatif metodu (çerçeve iskeleti) önermektedir. Önerilen yöntemin temelini, eksikliklerini azaltmak için ara katman karar yazılımı ekleyerek elle sayım yöntemine hemocytomer tabanlı otomatik saymanın uyarlanmasıdır. Buna ek olarak, önerilen yöntemimizi hücre sayımı (boyasız) ve hücre yaşayabilirliği analizi (boyalı) açısından test etmek için iki yeni veri seti toplanmıştır. Bu veri kümeleri, 'biyokimyasal.atilim.edu.tr/datasets/' adresinden kâr amacı gütmeyen herkesin kullanımına sunulmaktadır ve bu da bu araştırma alanındaki gelecek çalışmalara temel teşkil edecektir. Her iki veri kümesi, iki farklı türde kanser hücresi görüntüsü, yani, beyaz renkli promiyelositik lösemi (HL60) ve kronik miyelojenik lösemi (K562) içerir. Deneysel sonuçlarımızdan yola çıkarak, yöntemimiz HL60 ve K562 kanser hücreleri için sırasıyla geri çağırma skorları açısından % 92 ve % 74'e kadar ulaşmaktadır. Deney sonuçları, önerilen yöntemin mevcut hücre sayımı yaklaşımlarına güçlü bir alternatif olabileceğini de doğrular.Conference Object Citation Count: 3An alternative method for cell counting;(2011) Özkan, Akın; Tora, Hakan; Tora,H.; Uyar,P.; Işcan,M.; Airframe and Powerplant Maintenance; Department of Electrical & Electronics EngineeringCell counts and classification of the cells play an important role in the field of microbiology and cell biology. Although there exists many counting processes for cells of interest in suspension, the most basic cell counting process is performed by a person via the microscope. For counting cells the simplest, widely used and the most economic method is the use of hemocytometer counting. In this study, the hemocytometer counting was used but the the cells were counted by a proposed image based approach. The developed technique herein uses neural network along with the Hough transform. © 2011 IEEE.Article Citation Count: 1Computer vision based automated cell counting pipeline: A case study for HL60 cancer cell on hemocytometer(Scientific Publishers of India, 2018) Özkan, Akın; İşgör,S.B.; Şengül, Gökhan; İşgör,Y.G.; İşgör, Sultan Belgin; Department of Electrical & Electronics Engineering; Computer Engineering; Chemical EngineeringCounting of cells can give useful information about the cell density to understand the concerning cell culture condition. Usually, cell counting can be achieved manually with the help of the microscope and hemocytometer by the domain experts. The main drawback of the manual counting procedure is that the reliability highly depends on the experience and concentration of the examiners. Therefore, computer vision based automated cell counting is an essential tool to improve the accuracy. Although the commercial automated cell counting systems are available in the literature, their high cost limits their broader usage. In this study, we present a cell counting pipeline for light microscope images based on hemocytometer that can be easily adapted to the various cell types. The proposed method is robust to adverse image and cell culture conditions such as cell shape deformations, lightning conditions and brightness differences. In addition, we collect a novel human promyelocytic leukemia (HL60) cancer cell dataset to test our pipeline. The experimental results are presented in three measures: recall, precision and F-measure. The method reaches up to 98%, 92%, and 95% based on these three measures respectively by combining Support Vector Machine (SVM) and Histogram of Oriented Gradient (HOG). © 2018, Scientific Publishers of India. All rights reserved.Conference Object Citation Count: 19Kinshipgan: Synthesizing of Kinship Faces from Family Photos by Regularizing a Deep Face Network(IEEE Computer Society, 2018) Özkan, Akın; Ozkan,A.; Department of Electrical & Electronics EngineeringIn this paper, we propose a kinship generator network that can synthesize a possible child face by analyzing his/her parent's photo. For this purpose, we focus on to handle the scarcity of kinship datasets throughout the paper by proposing novel solutions in particular. To extract robust features, we integrate a pre-trained face model to the kinship face generator. Moreover, the generator network is regularized with an additional face dataset and adversarial loss to decrease the overfitting of the limited samples. Lastly, we adapt cycle-domain transformation to attain a more stable results. Experiments are conducted on Families in the Wild (FIW) dataset. The experimental results show that the contributions presented in the paper provide important performance improvements compared to the baseline architecture and our proposed method yields promising perceptual results. © 2018 IEEE.Master Thesis Işık mikroskobu kullanarak hücre sayımı için alternatif bir görüntü işleme yaklaşımı(2011) Özkan, Akın; Tora, Hakan; Tora, Hakan; İşgör, S. Belgin; Airframe and Powerplant Maintenance; Department of Electrical & Electronics EngineeringHücre sayımı ve bu hücrelerin sınıflandırılması için kullanılan yöntemler mikro biyoloji ve hücre biyolojisi alanında önemli bir yer tutmaktadır. En temel sayma mikroskop aracılığıyla Hemositometre kullanılarak insan tarafından yapılır. Bu süreçte hücre sayısı ve canlılığını belirlemek için kullanılan en ekonomik ve en yaygın teknik boya dışlama yöntemidir. Bu çalışmada, hücre canlı-ölü ayrımı yapabilen yeni bir görüntü tabanlı hücre sayımı yaklaşımı (NIBA-C) önerilmiştir. Önerilen yöntemin başarısını değerlendirmek için aynı görüntüler, yöntem ile elde edilen değerler klasik boya dışlama yöntemi ile elde edilen sonuçlar ile karşılaştırılmıştır. Yöntemi segmentasyon ve ardından görüntülerin sınıflandırılması oluşturur. Segmentasyon aşamasında Hough Dönüşümü kullanılmıştır. Yapay Sinir Ağları hücre-hücre olmayan ve canlı-ölü hücre görüntü sınıflandırmasında kullanılmıştır.Bu çalışmada; önerilen yöntem NIBA-C %70 in üzerinde yerbulma ve %50 üzerinde canlı ölü ayrımı yapabilme yetenegi sergilemiştir.Article Citation Count: 9Benchmarking Classification Models for Cell Viability on Novel Cancer Image Datasets(Bentham Science Publ Ltd, 2019) İşgör, Sultan Belgin; Şengül, Gökhan; Sengul, Gokhan; Isgor, Yasemin Gulgun; Özkan, Akın; Chemical Engineering; Computer Engineering; Department of Electrical & Electronics EngineeringBackground: Dye-exclusion based cell viability analysis has been broadly used in cell biology including anticancer drug discovery studies. Viability analysis refers to the whole decision making process for the distinction of dead cells from live ones. Basically, cell culture samples are dyed with a special stain called trypan blue, so that the dead cells are selectively colored to darkish. This distinction provides critical information that may be used to expose influences of the studied drug on considering cell culture including cancer. Examiner's experience and tiredness substantially affect the consistency throughout the manual observation of cell viability. The unsteady results of cell viability may end up with biased experimental results accordingly. Therefore, a machine learning based automated decision-making procedure is inevitably needed to improve consistency of the cell viability analysis. Objective: In this study, we investigate various combinations of classifiers and feature extractors (i.e. classification models) to maximize the performance of computer vision-based viability analysis. Method: The classification models are tested on novel hemocytometer image datasets which contain two types of cancer cell images, namely, caucasian promyelocytic leukemia (HL60), and chronic myelogenous leukemia (K562). Results: From the experimental results, k-Nearest Neighbor (KNN) and Random Forest (RF) by combining Local Phase Quantization (LPQ) achieve the lowest misclassification rates that are 0.031 and 0.082, respectively. Conclusion: The experimental results show that KNN and RF with LPQ can be powerful alternatives to the conventional manual cell viability analysis. Also, the collected datasets are released from the "biochem.atilim.edu.tr/datasets/ " web address publically to academic studies.
