Yazar "Yalcinkaya, Fikret" seçeneğine göre listele

Listeleniyor 1 - 8 / 8
  • Küçük Resim
    Öğe
    Classification of Uroflowmetry and EMG Signals of Pediatric Patients using Artificial Neural Networks
    (Ieee, 2017) Yalcinkaya, Fikret; Caliskan, Ozan; Erogul, Osman; Irkilata, Cem; Kopru, Burak; Coguplugil, Emrah
    UF-EMG test, in which non-invasive uroflowmetry (UF) and electromyography (EMG) signals are simultaneously recorded, is frequently used in children diagnosed with lower urinary tract dysfunction disease (AUSD) and its treatment. In the literature, independent (single) UF signals and integrated (dual) UF-EMG signals are graded many times but there is no classification study of UF-EMG integrated signals with Artificial Neural Networks (ANN), although studies have been done to classify UF signals with ANN. In this paper, it was aimed to classify the UF-EMG signals recorded from pediatric patients during the UF-EMG tests in Urodinami Center of Gulhane Education and Research Hospital using ANN. 773 (80%) of the 967 patients with an average age of 8 were used for training and 194 (20%) were used for the test. In YSA, the contribution of the features obtained from the EMG signals played a crucial role and was the main reason to improve the signal classification from 58% to 84.02%. The new classification method created by the obtained data does facilitate the interpretation of UF-EMG results for the clinical personnel in diagnosis, follow-up and treatment of patients. It is also aimed that the pediatric patients living in regions with less access to health care can be treated by providing an early and easy preliminary diagnostic tool.
  • [ X ]
    Öğe
    Convolutional Neural Network and Fuzzy Logic-based Hybrid Melanoma Diagnosis System
    (Kaunas Univ Technology, 2021) Yalcinkaya, Fikret; Erbas, Ali
    Studies on the detection of early stage melanoma have recently gained significant interest. Computer aided diagnosis systems based on neural networks, machine learning, convolutional neural networks (CNNs), and deep learning help early stage detection considerably. The colour and shapes of the images created by the pixels are crucial for the CNNs, as the pixels and associated pictures are interrelated just as a person's fingerprint is unique. By observing this relationship, the pixel values of each picture with its neighborhoods were determined by a fuzzy logic-based system and a unique fingerprint matrix named Fuzzy Correlation Map (FCov-Map) was produced. The fuzzy logic system has four inputs and one output. The advantage of CNNs trained with fuzzy covariance maps is to eliminate both the limited availability of medical grade training data and the need for extensive image preprocessing. The fuzzy logic output is fed to the pretrained AlexNet CNN algorithm. To deliver a reliable result, a deep CNN needs a large amount of data to process. However, to obtain and use the required sufficient data for diseases is not cost- and time-effective. Therefore, the suggested fuzzy logic-based fuzzy correlation map is tackling this issue to solve the limitedness of training CNN data set.
  • Küçük Resim
    Öğe
    The design of an embedded spinal cord stimulator
    (Tubitak Scientific & Technical Research Council Turkey, 2014) Yalcinkaya, Fikret; Erbas, Ali
    Spinal cord stimulation is a physical therapy methodology utilizing electrical impulses, pulses, or a combination of various standard electrical waveforms to block pain. However, standard forms are not functioning effectively for each illness due to the unique conditions of the patient. Therefore, patient-specific waveforms (or user-defined waveforms) integrated with nondestructive, complete, or partially noninvasive and effective medical instruments to help relieve pain are required. In the literature, 2 different designs have been discussed: the bedside/portable and the implantable/surgical types. This work is introducing a new hybrid type: a portable touch-screen multichannel embedded spinal cord stimulator. This work is made of 3 parts: the hardware and the software designs, and an embedded interface introducing the system to external medical systems or patients. The hardware and software designs are explained in detail. The S3C2440 microprocessor-based embedded spinal cord stimulator generates not only 6 standards types of signals, but also user defined waveform(s). This requires medical experts' close relation, consultation, and cooperation with biomedical engineers who are able to design and develop new instruments with new requirements. In this paper, a microprocessor-based spinal cord stimulator is developed in the Samsung S3C2440 environment and PIC18F452-based channel boards. The S3C2440 environment is the main controller unit and the therapy signal is produced by the channel boards as a signal generator. The software is prepared by MS Visual Studio 2008 and Hi-Tech C. The frequency, duty-cycle, and amplitudes of the pulses can be altered by software control. The architecture of the stimulators is designed to be modular; therefore, its different blocks can be reused as standard building blocks. The designed and developed embedded spinal cord stimulator enables both home and bedside health care. The system is battery-operated, portable, user-friendly, and cost-effective.
  • [ X ]
    Öğe
    Image Masking and Enhancement System for Melanoma Early Stage Detection
    (Tech Science Press, 2022) Yalcinkaya, Fikret; Erbas, Ali
    Early stage melanoma detection (ESMD) is crucial as late detection kills Computer aided diagnosis systems (CADS) integrated with high level algorithms are major tools capable of ESMD with high degree of accuracy, specificity, and sensitivity. CADS use the image and the information within the pixels of the image. Pixels' characteristics and orientations determine the colour and shapes of the images as the pixels and associated environment are closely interrelated with the lesion. CADS integrated with Convolutional Neural Networks (CNN) specifically play a major role for ESMD with high degree of accuracy. The proposed system has two steps to produce high degree of accuracy; the first is the Fuzzy-Logic (FL) based maskProd and pixel Enhancement vectors and the second is integration with deep CNN algorithms. The vectors, maskProd and pixel Enhancement, are based on lesion masking and image enhancement via fuzzy logic as hSvMask production has three inputs; sChannel, entropyFilter and distanceVector. The originality of this paper is based on the regional enhancement of each pixel of the image by fuzzy-logic. To change the brightness of an image by the conventional methods (CM), it is required to multiply the value of all and each pixels of the image by the same constant or weight value in order to generate new values of the pixels. But in the proposed method, FL determines a special and specific constant or weight value for every single pixel of the image; called a variable weight value. Secondly via conventional or classical methods of image processing, mask production is realized based on the threshold value predetermined, and this threshold value decides whether the image lesion is melanoma or not. CM use constant weight values but in the method suggested, FL determines a specific variable weight value for every single pixel of the image. Secondly CMs realize the mask production using predetermined threshold values, but proposed method suggests a new way capable of detecting pixels in the lesion-free regions, to do that, a system using FL was developed for each and every single pixel via the data obtained from three sources: the s-channel of the HSV (Hue, Saturation and Value) colour space, distance vector, and image entropy vector. The proposed method and its integration with algorithms; such as AlexNet, GoogLeNet, Resnetl8, VGG-16, VGG-19, Inception-V3, ShuffleNet and Xception, detected the melanoma with an accuracy of 86.95, 85.60, 86.45, 86.70, 85.47, 86.76, 86.45 and 85.30 percentages, respectively.
  • [ X ]
    Öğe
    Kidney Tumor Segmentation Using Two-Stage Bottleneck Block Architecture
    (Tech Science Press, 2022) Turk, Fuat; Luy, Murat; Barisci, Necaattin; Yalcinkaya, Fikret
    Cases of kidney cancer have shown a rapid increase in recent years. Advanced technology has allowed bettering the existing treatment methods. Research on the subject is still continuing. Medical segmentation is also of increasing importance. In particular, deep learning-based studies are of great importance for accurate segmentation. Tumor detection is a relatively difficult procedure for soft tissue organs such as kidneys and the prostate. Kidney tumors, specifically, are a type of cancer with a higher incidence in older people. As age progresses, the importance of having diagnostic tests increases. In some cases, patients with kidney tumors may not show any serious symptoms until the last stage. Therefore, early diagnosis of the tumor is important. This study aimed to develop support systems that could help physicians in the segmentation of kidney tumors. In the study, improvements were made on the encoder and decoder phases of the V-Net model. With the double-stage bottleneck block structure, the architecture was transformed into a unique one, which achieved an 86.9% kidney tumor Dice similarity coefficient. The results show that the model gives applicable and accurate results for kidney tumor segmentation.
  • Küçük Resim
    Öğe
    Mathematical Modelling of Human Heart as a Hydroelectromechanical System
    (Ieee, 2013) Yalcinkaya, Fikret; Kizilkaplan, Ertem; Erbas, Ali
    Different electrical models of human heart, partial or complete, with linear or nonlinear models have been developed. In the literature, there are some applications of mathematical and physical analog models of total artificial heart (TAH), a baroreceptor model, a state-space model, an electromechanical biventricular model of the heart, and a mathematical model for the artificial generation of electrocardiogram (ECG) signals. Physical models are suitable to simulate real physiological data based on proper experimental set up present. This paper introduces a new mathematical modelling of human heart as a hydroelectromechanical system (HEMS). This paper simulates the human heart based on three main functions: hydraulic, electrical and mechanical parameters. Hydro-mechanical model developed then has been transformed into electrical domain and simulation has been carried out according to the mathematical model or formulations obtained using Laplace transform. This electrical model / circuit is then tested by MATLAB based simulations and results found are comparable with the normal ECG waveforms so that these simulated results may be useful in clinical experiments. In this model basic electrical components have been used to simulate the physiological functions of the human heart. The result is a simple electrical circuit consisting of main electrical parameters that are transformed from hydraulic models and medical physiological values. Developed MATLAB based mathematical model will primarely help to understand the proper functioning of an artificial heart and its simulated ECG signals. A comprehensive model for generating a wide variety of such signals has been targeted for future in this paper. This research especially focuses on modelling human heart as a hydro-electro-mechanical system with three case studies.
  • Küçük Resim
    Öğe
    Matlab/Simulink Based Comparative Analysis of the Effect of Ion Concentration on Action Potantial by Using Hodgkin-Huxley and Morris-Lecar Neuron Models
    (Ieee, 2017) Yalcinkaya, Fikret; Unsal, Hamza
    In pharmacological experiments, the effect of ion concentrations on Action potential (AP) and its association with important neurological disorders such as stroke, epilepsy and depression has been investigated. In this paper, Hodgkin-Huxley (HH) and Morris-Lecar (ML) membrane models, which have contributed to the literature of theoretical neural science, have been studied. In the first step, these two models have been simulated individually in Simulink environment using Matlab software. In the second step, the ratio of the amount of extracellular ion to the amount of intracellular ion was expressed as Hd/Hi. Based on Na. and K. as ions, the effect of Hd/Hi ratio change on AP signal was investigated based on HH and ML as a model. In the HH model, it was searched that the Hd/Hi change due to sodium ions influenced the amplitude of AP and the depolarization phase. The change in Hd/Hi ratio due to potassium ion concentrations was found to affect the threshold and hyper-polarization phase of AP. In the ML membrane model, it was observed that the change in Hd/Hi ratio due to intracellular and extracellular Ca+2 and K- ion concentrations affected the formation time of AP, firing time, amplitude and formation phases. HH and ML models showed similarity in the same parameters.
  • Küçük Resim
    Öğe
    Wide Area Scanning Trap Camera System with Multi-Cameras and Distinctive Motion Detection Sensor
    (Ieee, 2018) Simsek, Murat; Yalcinkaya, Fikret; Ugurlutan, Rifat
    The Trap Camera Systems in lots of different fields such as defense, medical, ecological research, wildlife surveillance, reconnaissance and surveillance, scientific research, nest ecology, rare species detection, ecological population estimation and rich species identification, border security, illegal migration detection, habitat occupation, orienteering, sports activities, military and civilian target detection and sporting events, provide limited situational awareness with single camera and motion detection sensor having ability Access restricted distances. Unlike the conventional systems, in this paper, we present a wide-area scanning system, which is created with four cameras and image processing software, designed to penetrate far distances with a unique designed motion detection sensors. The designed system is adaptive and compact and extra features can be optionally added with completely original software.