WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Use of Laser-Induced Bubbles in Intraocular Pressure Measurement: A Preliminary Study(IOP Publishing Ltd, 2018-11-23) Altindis, Fatih; Ozdur, Ibrahim T.; Mutlu, Sait N.; Yilmaz, BulentThis work investigates the feasibility of a novel approach for measuring intraocular pressure (IOP) by analyzing micron-level laser-induced bubble characteristics in the intraocular fluid. We believe that this concept may be used as a non-invasive alternative for measuring a patient's IOP by analyzing the laser-induced bubble volume in the intraocular fluid in the anterior chamber of the eye. The behavior of laser-induced bubbles was examined under differing fluid pressure levels and at differing laser pulse energy levels. An intraocular medium-like environment was imitated and an imaging system was designed in order to capture laser-induced bubbles with their movements. The video recordings of the bubbles were processed using custom software, and the volume of the bubbles was estimated using three different approaches. The bubble volumes were estimated more accurately by using the rising velocity of the bubble rather than its direct radii appearances on the images. An inversely proportional relationship was observed between the laser-induced bubble volume and the fluid pressure. IOP can be measured with a non-invasive technique using laser-induced bubble volume. Deeper and detailed studies, including clinical studies, may lead to the use of lasers for measuring IOP.Article Citation - WoS: 1Citation - Scopus: 1Prediction of Preference and Effect of Music on Preference: A Preliminary Study on Electroencephalography from Young Women(Tubitak Scientific & Technological Research Council Turkey, 2019-03-01) Yilmaz, Bulent; Gazeloglu, Cengiz; Altindis, FatihNeuromarketing is the application of the neuroscientific approaches to analyze and understand economically relevant behavior. In this study, the effect of loud and rhythmic music in a sample neuromarketing setup is investigated. The second aim was to develop an approach in the prediction of preference using only brain signals. In this work, 19-channel EEG signals were recorded and two experimental paradigms were implemented: no music/silence and rhythmic, loud music using a headphone, while viewing women shoes. For each 10-sec epoch, normalized power spectral density (PSD) of EEG data for six frequency bands was estimated using the Burg method. The effect of music was investigated by comparing the mean differences between music and no music groups using independent two-sample t-test. In the preference prediction part sequential forward selection, k-nearest neighbors (k-NN) and the support vector machines (SVM), and 5-fold cross-validation approaches were used. It is found that music did not affect like decision in any of the power bands, on the contrary, music affected dislike decisions for all bands with no exceptions. Furthermore, the accuracies obtained in preference prediction study were between 77.5 and 82.5% for k-NN and SVM techniques. The results of the study showed the feasibility of using EEG signals in the investigation of the music effect on purchasing behavior and the prediction of preference of an individual.Article Citation - WoS: 8Citation - Scopus: 10Lung Cancer Subtype Differentiation From Positron Emission Tomography Images(Tubitak Scientific & Technological Research Council Turkey, 2020-01-27) Ayyildiz, Oguzhan; Aydin, Zafer; Yilmaz, Bulent; Karacavus, Seyhan; Senkaya, Kubra; Icer, Semra; Kaya, Eser; Taşdemir, ArzuLung cancer is one of the deadly cancer types, and almost 85% of lung cancers are nonsmall cell lung cancer (NSCLC). In the present study we investigated classification and feature selection methods for the differentiation of two subtypes of NSCLC, namely adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). The major advances in understanding the effects of therapy agents suggest that future targeted therapies will be increasingly subtype specific. We obtained positron emission tomography (PET) images of 93 patients with NSCLC, 39 of which had ADC while the rest had SqCC. Random walk segmentation was applied to delineate three-dimensional tumor volume, and 39 texture features were extracted to grade the tumor subtypes. We examined 11 classifiers with two different feature selection methods and the effect of normalization on accuracy. The classifiers we used were the k-nearest-neighbor, logistic regression, support vector machine, Bayesian network, decision tree, radial basis function network, random forest, AdaBoostM1, and three stacking methods. To evaluate the prediction accuracy we performed a leave-one-out cross-validation experiment on the dataset. We also considered optimizing certain hyperparameters of these models by performing 10-fold cross-validation separately on each training set. We found that the stacking ensemble classifier, which combines a decision tree, AdaBoostM1, and logistic regression methods by a metalearner, was the most accurate method for detecting subtypes of NSCLC, and normalization of feature sets improved the accuracy of the classification method.Article Citation - WoS: 15Citation - Scopus: 18Histopathology Image Classification: Highlighting the Gap Between Manual Analysis and AI Automation(Frontiers Media S.A., 2024-01-17) Dogan, Refika Sultan; Yilmaz, BulentThe field of histopathological image analysis has evolved significantly with the advent of digital pathology, leading to the development of automated models capable of classifying tissues and structures within diverse pathological images. Artificial intelligence algorithms, such as convolutional neural networks, have shown remarkable capabilities in pathology image analysis tasks, including tumor identification, metastasis detection, and patient prognosis assessment. However, traditional manual analysis methods have generally shown low accuracy in diagnosing colorectal cancer using histopathological images. This study investigates the use of AI in image classification and image analytics using histopathological images using the histogram of oriented gradients method. The study develops an AI-based architecture for image classification using histopathological images, aiming to achieve high performance with less complexity through specific parameters and layers. In this study, we investigate the complicated state of histopathological image classification, explicitly focusing on categorizing nine distinct tissue types. Our research used open-source multi-centered image datasets that included records of 100.000 non-overlapping images from 86 patients for training and 7180 non-overlapping images from 50 patients for testing. The study compares two distinct approaches, training artificial intelligence-based algorithms and manual machine learning models, to automate tissue classification. This research comprises two primary classification tasks: binary classification, distinguishing between normal and tumor tissues, and multi-classification, encompassing nine tissue types, including adipose, background, debris, stroma, lymphocytes, mucus, smooth muscle, normal colon mucosa, and tumor. Our findings show that artificial intelligence-based systems can achieve 0.91 and 0.97 accuracy in binary and multi-class classifications. In comparison, the histogram of directed gradient features and the Random Forest classifier achieved accuracy rates of 0.75 and 0.44 in binary and multi-class classifications, respectively. Our artificial intelligence-based methods are generalizable, allowing them to be integrated into histopathology diagnostics procedures and improve diagnostic accuracy and efficiency. The CNN model outperforms existing machine learning techniques, demonstrating its potential to improve the precision and effectiveness of histopathology image analysis. This research emphasizes the importance of maintaining data consistency and applying normalization methods during the data preparation stage for analysis. It particularly highlights the potential of artificial intelligence to assess histopathological images.Article Citation - WoS: 7Citation - Scopus: 5Effect of Interpolation on Specular Reflections in Texture-Based Automatic Colonic Polyp Detection(Wiley, 2020-06-26) Kacmaz, Rukiye Nur; Yilmaz, Bulent; Aydin, ZaferReflections of LED light cause unwanted noise effects called specular reflection (SR) on colonoscopic images. The aim of this study was to seek answers to the following two questions. (a) How are the texture features used in automatic detection of polyps affected by the interpolation on specular reflections? (b) If they are affected does it really affect the classification performance? In order to answer these questions, we used 610 colonoscopy images, and divided each image into tiles whose sizes were 32-by-32 pixels. From these tiles, we selected the ones without any specular reflection. We added different shape and size specular reflections cropped from real images onto the reflection-free tiles. We then used the nearest neighbors, bilinear and bicubic interpolation techniques on the tiles on which SRs were added. On these tiles we extracted 116 texture features using 3 second-order approaches, and 4 first-order statistics. First, we used paired samplettest. Second, we performed automatic classification of polyps and background using random forest and k nearest neighbors (k-NN) approaches using the texture features for different combinations of specular reflections added on the tiles from the polyp or background. The results showed that depending on the size of specular reflection, interpolation can cause a significant difference between the texture features that were coming from reflection-free tiles and the same tiles on which interpolation was performed. In addition, we note that bicubic interpolation may be preferred to eliminate specular reflection when texture features are used for background and polyp discrimination.Conference Object Citation - WoS: 1Automatic Blurry Colon Image Detection Using Laplacian Operator-Based Features(Elsevier Science Bv, 2018-08) Yilmaz, Bulent; Kacmaz, Rukiye Nur; Dundar, Mehmet SaitArticle Citation - WoS: 5Citation - Scopus: 10A New Tool for QT Interval Analysis During Sleep in Healthy and Obstructive Sleep Apnea Subjects: A Study on Women(Tubitak Scientific & Technological Research Council Turkey, 2013) Kaya, Kemal Alican; Yilmaz, BulentBy monitoring the Q wave/T wave (QT) interval computed from electrocardiography (ECG) signals during sleep, it is possible to create a link between the ventricular repolarization and sleep stages. In this study, we aimed to find a robust and simple approach to automatically determine the fiducials on each 30-s sleep epoch, such as the Q, R, and T-end points, on long sleep ECG recordings in order to statistically analyze the effect of obstructive sleep apnea (OSA) and sleep stages on QT intervals. This is a retrospective study in which the ECG data extracted from the polysomnography recordings of 7 healthy women and 5 women with OSA, acquired in a sleep laboratory, were used. Experts annotated the sleep stage and OSA presence information for each 30-s epoch. Later, we visually selected epochs with clean signals from a total of 8324 epochs. On the selected epochs, we determined R peaks on each heartbeat, and by aligning each ECG portion corresponding to a heartbeat using those R points, we computed an average ECG signal for each epoch. On the average ECG signals, we developed a novel approach to find the Q and T-end points. With the help of Bazzet's formula, we computed the corrected QT interval (QTc) values for each epoch using the QT and the median RR interval. Finally, we analyzed the QTc values for the different sleep stages and healthy or OSA groups. We employed statistical approaches such as the Mann-Whitney U test, Freidman's test, and the Wilcoxon signed-rank test. As a result of this study, we found that OSA has a prolongation effect on the total duration of the ventricular depolarization and repolarization. We also observed that the QTc values computed in each sleep stage were significantly different between the healthy and OSA groups. Additionally, we discovered that within the healthy group, the QTc values were distinctive in the different sleep stages.