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Deniz Karacor

Bio: Deniz Karacor is an academic researcher from Ankara University. The author has contributed to research in topics: Lissajous curve & Time domain. The author has an hindex of 5, co-authored 7 publications receiving 159 citations.

Papers
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Journal ArticleDOI
TL;DR: A new and completely automatic counting, segmentation and classification process is developed that automatically counts the white blood cells, determine their sizes accurately and classifies them into five types such as basophil, lymphocyte, neutrophil, monocyte and eosinophil.

135 citations

Journal ArticleDOI
TL;DR: An innovative method based on an algorithm utilizing discrete convolutions of discrete-time functions is developed to obtain and represent discrete Lissajous and recton functions, which are actually discrete auto- and cross-correlation functions.
Abstract: In this paper, an innovative method based on an algorithm utilizing discrete convolutions of discrete-time functions is developed to obtain and represent discrete Lissajous and recton functions. They are actually discrete auto- and cross-correlation functions. The theory of discrete Lissajous figures is developed. The concept of rectons is introduced. The relation between the discrete Lissajous figures and autocorrelation functions is set. Some applications are described including phase, frequency, and period determination of periodic signals, time-domain characteristics (such as damping ratio) of a control system, and abnormality and spike detection within a signal, are described. In addition, an electrocardiogram signal with an abnormality of atrial fibrillation is given for abnormality detection by means of recton functions. An epileptic activity detection within an electroencephalography signal is also given.

19 citations

Journal ArticleDOI
TL;DR: In this paper, the authors used two kinds of neural network structures: MultiLayer Perceptron (MLP) and Radial Basis Function (RBF) network types for training of the neural networks.

16 citations

Journal Article
TL;DR: The results of the classification process for which a study with hundreds of images of white blood cells was experienced are given, which will help to diagnose especially ALL disease in a fast and automatic way.
Abstract: Determination and classification of white blood cells are very important for diagnosing many diseases. The number of white blood cells and morphological changes or blasts of them provide valuable information for the positive results of the diseases such as Acute Lymphocytic Leucomia (ALL). Recognition and classification of white cells as basophils, lymphocytes, neutrophils, monocytes and eosinophils also give additional information for the diagnosis of many diseases. We are developing an automatic process for counting, size determination and classification of white blood cells. In this paper, we give the results of the classification process for which we experienced a study with hundreds of images of white blood cells. This process will help to diagnose especially ALL disease in a fast and automatic way. Three methods are used for classification of five types of white blood cells. The first one is a new algorithm utilizing image matching for classification that is called the Speed-Up Robust Feature detector (SURF). The second one is the PCA that gives the advantage of dimension reduction. The third is the classification tree called dendrogram following the PCA. Satisfactory results are obtained by two techniques.

10 citations

Journal ArticleDOI
TL;DR: A new magnetic measurement system is developed to determine upper surfaces of buried magnetic materials, particularly land mines, and uses sensor images to identify various ferromagnetic materials and similar objects using the speeded-up-feature-transform algorithm.
Abstract: In this paper, a new magnetic measurement system is developed to determine upper surfaces of buried magnetic materials, particularly land mines. This measurement system uses the magnetic-anomaly-detection method. It also has intelligent identification software based on an image matching algorithm. It is aimed to determine and identify the buried ferromagnetic materials with minimum energy consumption. It is concentrated on the detection and identification of the shapes of upper surfaces of buried magnetic materials in dry and wet conditions. The effect of humidity in the detection process for detection is tested. In this paper, we used sensor images to identify various ferromagnetic materials and similar objects. Sensor images of soils at various humidities covering the objects were obtained. We used the speeded-up-feature-transform algorithm in the comparison process of the images. Dry soil sample images match with the corresponding wet soil samples with the highest matching rate. The images for different objects can easily be distinguished by the matching process.

7 citations


Cited by
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Book ChapterDOI
01 Jan 2001
TL;DR: In this paper, the theory of the feedback principle and its application in a carrier-in-cable system were described and explained. And the results of this trial were highly satisfactory and demonstrated conclusively the correctness of the theory and the practicability of its commercial application.
Abstract: This paper describes and explains the theory of the feedback principle and then demonstrates how stability of amplification and reduction of modulation products, as well as certain other advantages, follow when stabilized feedback is applied to an amplifier. The underlying principle of design by means of which singing ia avoided is next set forth. The paper concludes with some examples of results obtained on amplifiers which have been built employing this new principle. The carrier-in-cable system dealt with in a companion paper1 involves many amplifiers in tandem with many telephone channels passing through each amplifier and constitutes, therefore, an ideal field for application of this feedback principle. A field trial of this system was made at Morristown, New Jersey, in which seventy of these amplifiers were operated in tandem. The results of this trial were highly satisfactory and demonstrated conclusively the correctness of the theory and the practicability of its commercial application.

279 citations

Journal ArticleDOI
TL;DR: A novel WBCs identification system based on deep learning theory is proposed and a high performance W BCsNet can be employed as a pre-trained network.

153 citations

Journal ArticleDOI
TL;DR: An algorithm to detect WBCs from the microscope images based on the simple relation of colors R, B and morphological operation is proposed and has better effect almost than iterative threshold method with less cost time, and some classification experiments show that the proposed classification method has better accuracy almost than some other methods.
Abstract: The detection and classification of white blood cells (WBCs, also known as Leukocytes) is a hot issue because of its important applications in disease diagnosis. Nowadays the morphological analysis of blood cells is operated manually by skilled operators, which results in some drawbacks such as slowness of the analysis, a non-standard accuracy, and the dependence on the operator’s skills. Although there have been many papers studying the detection of WBCs or classification of WBCs independently, few papers consider them together. This paper proposes an automatic detection and classification system for WBCs from peripheral blood images. It firstly proposes an algorithm to detect WBCs from the microscope images based on the simple relation of colors R, B and morphological operation. Then a granularity feature (pairwise rotation invariant co-occurrence local binary pattern, PRICoLBP feature) and SVM are applied to classify eosinophil and basophil from other WBCs firstly. Lastly, convolution neural networks are used to extract features in high level from WBCs automatically, and a random forest is applied to these features to recognize the other three kinds of WBCs: neutrophil, monocyte and lymphocyte. Some detection experiments on Cellavison database and ALL-IDB database show that our proposed detection method has better effect almost than iterative threshold method with less cost time, and some classification experiments show that our proposed classification method has better accuracy almost than some other methods.

151 citations

Journal ArticleDOI
TL;DR: A computer-aided automated system that can easily identify and locate WBC types in blood images has been proposed and showed 100% success in determining WBC cells.

138 citations

Proceedings ArticleDOI
13 Apr 2018
TL;DR: This work considers the problem of pre-processing and supervised classification of white blood cells into their four primary types including Neutrophils, Eosinophils, Lymphocytes, and Monocytes using a consecutive proposed deep learning framework and seeks to determine a fast, accurate mechanism for classification.
Abstract: This works gives an account of evaluation of white blood cell differential counts via computer aided diagnosis (CAD) system and hematology rules. Leukocytes, also called white blood cells (WBCs) play main role of the immune system. Leukocyte is responsible for phagocytosis and immunity and therefore in defense against infection involving the fatal diseases incidence and mortality related issues. Admittedly, microscopic examination of blood samples is a time consuming, expensive and error-prone task. A manual diagnosis would search for specific Leukocytes and number abnormalities in the blood slides while complete blood count (CBC) examination is performed. Complications may arise from the large number of varying samples including different types of Leukocytes, related sub-types and concentration in blood, which makes the analysis prone to human error. This process can be automated by computerized techniques which are more reliable and economical. In essence, we seek to determine a fast, accurate mechanism for classification and gather information about distribution of white blood evidences which may help to diagnose the degree of any abnormalities during CBC test. In this work, we consider the problem of pre-processing and supervised classification of white blood cells into their four primary types including Neutrophils, Eosinophils, Lymphocytes, and Monocytes using a consecutive proposed deep learning framework. For first step, this research proposes three consecutive pre-processing calculations namely are color distortion; bounding box distortion (crop) and image flipping mirroring. In second phase, white blood cell recognition performed with hierarchy topological feature extraction using Inception and ResNet architectures. Finally, the results obtained from the preliminary analysis of cell classification with (11200) training samples and 1244 white blood cells evaluation data set are presented in confusion matrices and interpreted using accuracy rate, and false positive with the classification framework being validated with experiments conducted on poor quality blood images sized 320 × 240 pixels. The deferential outcomes in the challenging cell detection task, as shown in result section, indicate that there is a significant achievement in using Inception and ResNet architecture with proposed settings. Our framework detects on average 100% of the four main white blood cell types using ResNet V1 50 while also alternative promising result with 99.84% and 99.46% accuracy rate obtained with ResNet V1 152 and ResNet 101, respectively with 3000 epochs and fine-tuning all layers. Further statistical confusion matrix tests revealed that this work achieved 1, 0.9979, 0.9989 sensitivity values when area under the curve (AUC) scores above 1, 0.9992, 0.9833 on three proposed techniques. In addition, current work shows negligible and small false negative 0, 2, 1 and substantial false positive with 0, 0, 5 values in Leukocytes detection.

136 citations