WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Vim-Polyp: Multimodal Colon Polyp Dataset with Video, Histopathology, and Protein Expression(Nature Portfolio, 2025-12-03) Dogan, Refika Sultan; Akay, Ebru; Dogan, Serkan; Yilmaz, BulentThe dataset in this study includes 202 videos with a total of 422 minutes, reaching Kayseri City Hospital's gastroenterology department as colonoscopy videos and 1903 microscopy images between 2019 and 2021. It includes 399 colonoscopy, microscopy images, and pathological diagnoses of polyps, as well as immunohistochemical staining results for proteins that play an important role in the assessment of cancerous cells, such as staining results for p53 (clone: bp53-11), Ki-67 (clone: 30-9), CD34 (clone: QBend/10), PD-L1 (clone: SP142), BRAF (clone: V600E) and VEGF (clone: SP125). By sharing the data openly, we aim to facilitate benchmarking, exploratory analysis and transfer-learning studies on colorectal polyps and cancer. In combination with external datasets or pretrained models, the resource can help advance data-driven detection and characterisation work. The diverse range of polyps assigned to cancer stages from 201 patients makes this tool valuable for researchers and clinicians in furthering diagnosis and treatment.Conference Object Real-Time Robotic Car Control Using Brainwaves and Head Movement(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2018) Ozturk, Nedime; Yilmaz, Bulent; Onver, Ahmet YasinEmotiv Epoc Headset is a portable and low-cost device. In this study, Emotiv Epoc headset was used in order to obtain real-time gyro and EEG signals. The aim of this study was to control a robotic car in real-time by using head movement and opening and closing of the eyes. The maximum and minimum amplitude of the gyro signal, and the ratios of the beta waves of O1 and O2 channel to alpha waves of the same channels were used as threshold values. These threshold values were used to determine the direction of the robotic car. Because of its low-cost and easy implementation, Arduino Uno was used to manage the robotic car. This study has shown that brain waves and head movements can control a device in real time. This system has the potential to be used in neurofeedback and brain-computer interface applications.Conference Object Effect of Bilinear Interpolation on the Texture Analysis of Colonoscopy Images(IEEE345 E 47TH ST, NEW YORK, NY 10017 USA, 2017) Kacmaz, Rukiye Nur; Yilmaz, BulentInterpolation is a method that is used to obtain unknown intensities with the help of known intensities on an image. This method is frequently used in the literature to eliminate light reflection on colonoscopy images. Texture features are the most important characteristics used to describe the region or objects of interest in the image. They are the measures of intensity variation of a surface that determine properties such as smoothness, roughness, and regularity. The aim of this study is to find out the how bilinear interpolation applied on colonoscopy images with reflection impact texture features obtained from the same images. A research carried out to make reasonable comparison between a texture feature from an image with no reflection and the same feature obtained from the same image with synthetically added reflections with various percentages. Using the approaches like gray level co-occurence matrix (GLCM), gray level run length matrix (GLRLM), neighborhood gray tone difference matrix (NGTDM) 126 features were extracted from each 32x32 sub-images coming from 610 colonoscopy images. Several of the features extracted from sub-images with no reflection and reflection were not statistically significantly different, while majority of them were affected from the reflections.Conference Object Detection of Variation Instances on Colonoscopy Videos using Structural Similarity Index(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2018) Kacmaz, Rukiye Nur; Yilmaz, BulentThe aim of this study is to reduce the number of images extracted from the videos recorded by the specialists during the colonoscopy process for further examination, thereby enabling the specialist to deal with fewer images. Since the images obtained from the videos are very similar, the main assumption of this study is that the whole video can be represented by fewer images. The approach used in this study is the structural similarity index. Totally, images were obtained from 4 different videos coming from healthy, ulcerative colitis, Crohn's, and polyp patients. The noisy images in these videos were eliminated manually. When the structural similarity index between two consecutive clear images was less than 0.83, the second image was selected and shown to the specialist for his/her examination. By this way, the frames carrying significantly new information from the videos were defined as the variation instances. The tests on healthy or diseased colon videos showed that only 5-10% of the clear images provide significantly new information.Conference Object Citation - WoS: 7Citation - Scopus: 12Use of Topological Data Analysis in Motor Intention Based Brain-Computer Interfaces(European Signal Processing Conference, EUSIPCO, 2018-09) Altindis, Fatih; Yilmaz, Bulent; İçöz, Kutay; Borisenok, S.This study aims to investigate the use of topological data analysis in electroencephalography (EEG) based on brain-computer interface (BCI) applications. Our study focused on extracting topological features of EEG signals obtained from the motor cortex area of the brain. EEG signals from 8 subjects were used for forming data point clouds with a real-time simulation scenario and then each cloud was processed with JPlex toolbox in order to find out corresponding Betti numbers. These numbers represent the topological structure of the point data cloud related to the persistent homologies, which differ for different motor activity tasks. The estimated Betti numbers has been used as features in k-NN classifier to discriminate left or right hand motor intentions. © 2019 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 1Citation - Scopus: 1Polyp Localization in Colonoscopy Images Using Vessel Density(Institute of Electrical and Electronics Engineers Inc., 2018-11) Doǧan, Refika Sultan; Yilmaz, BulentIn this paper, we present a new approach for polyp localization in colonoscopy images. This approach is based on the determination of the polyp location using the vessel density in colon images. Primarily, we used pre-processing procedures on the colon images, and then blood vessel extraction techniques were employed. Later, segmentation of the vessel boundaries was performed. With the help of vessel boundaries we calculated the vessel density, and used this for the localization of the polyps. We tested the success of this approach using a publicly available image set (CVC-ClinicDB database). This database consisted of 612 images from 29 different polyps. This approach succeeds in correct detection of 24 out of 29 different polyps. © 2019 Elsevier B.V., All rights reserved.Article Citation - WoS: 3Citation - Scopus: 3Object Weight Perception in Motor Imagery Using Fourier-Based Synchrosqueezing Transform and Regularized Common Spatial Patterns(IEEE-Inst Electrical Electronics Engineers Inc, 2024) Karakullukcu, Nedime; Altindis, Fatih; Yilmaz, BulentThis study addresses the challenge faced by individuals with upper-limb prostheses in regulating grip force and adapting movements to different object weights. Despite limited exploration, this research pioneers the use of EEG to estimate object weight perception in the context of upper-limb prostheses. Investigating neural correlates in this population provides valuable insights and aids the development of neurofeedback-based strategies for weight perception. Our objective is to identify EEG features predicting the weight perception of held objects. Employing Fourier-based synchrosqueezing transform (FSST) and regularized Common Spatial Patterns (CSP) features, we classify motor imagery waves representing three weight categories (light, medium, heavy). Subjects perform actual motor tasks before imagery sessions, and our approach integrates EEG features of both movements to train subject-specific machine learning models. Results reveal that FSST- singular value decomposition (SVD) features for medium and heavy objects are most distinctive. Achieving up to 90% accuracy, spatial features demonstrate effective classification of motor imagery for different weights. Unlike weight prediction studies, our focus is on visual perception and imagination of object weights, enhancing prosthetic hand system preconditioning. Binary classification surpasses 70% accuracy in predicting object weights, uniquely utilizing actual movement data for CSP algorithm regularization coefficient estimation.Conference Object Multivariate Pseudo Wigner Ville Distribution Based Emotion Detection From Electrical Activity of Brain(IEEE, 2017) Ozel, Pinar; Akan, Aydin; Yilmaz, BulentRecently, there has been a rapid development in multivariate signal analysis to determine joint oscillations for multiple data channels. The emotion elicitation in an electroencephalogram (EEG) is a novel area to evaluate methods for emotional differences from brain signals. In this paper, utilizing the idea of joint instantaneous frequency of multivariate data, a multivariate extension of pseudo Wigner distribution is used for emotion recognition from EEG signals, in which different window sizes are employed to interpret the results. As a preliminary study, the best results are obtained as 90%, 75%, 65% in terms of valence, arousal and dominance scale respectively for larger window size.Conference Object Citation - WoS: 3Citation - Scopus: 3Examining Tongue Movement Intentions in EEG With Machine and Deep Learning: An Approach for Dysphagia Rehabilitation(IEEE, 2024-08-26) Aslan, Sevgi Gokce; Yilmaz, BulentDysphagia, a common swallowing disorder particularly prevalent among older adults and often associated with neurological conditions, significantly affects individuals' quality of life by negatively impacting their eating habits, physical health, and social interactions. This study investigates the potential of brain-computer interface (BCI) technologies in dysphagia rehabilitation, focusing specifically on motor imagery paradigms based on EEG signals and integration with machine learning and deep learning methods for tongue movement. Traditional machine learning classifiers, such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Naive Bayes, Random Forest, AdaBoost, Bagging, Kernel, and Neural Network were employed in discrimination of rest and imagination phases of EEG signals obtained from 30 healthy subjects. Scalogram images obtained using continuous wavelet transform of EEG signals corresponding to the rest and imagination phases of the experiment were used as the input images to the CNN architecture. As a result, KNN and SVM, exhibited lower accuracy rates compared to ensemble methods like AdaBoost and Random Forest, which are effective in handling complex datasets. Additionally, a deep learning approach achieved an accuracy rate of 83%. Overall, this study demonstrates the promising role of BCI technologies and machine learning techniques in dysphagia rehabilitation.Conference Object Citation - WoS: 1Citation - Scopus: 3Emotional State Analysis From EEG Signals via Noise-Assisted Multivariate Empirical Mode Decomposition Method(IEEE, 2017) Ozel, Pinar; Akan, Aydin; Yilmaz, BulentEmotional state analysis is an interdisciplinary arena because of the many parameters that encompass the complex neural structure and electrical signals of the brain and in terms of emotional state differences. In recent years, emotional state data have been examined by using data-driven methods such as Empirical Mode Decomposition as well as classical time-frequency methods. Although Empirical Mode Decomposition has many advantages, it has disadvantages such as being designed for univariate data, prone to mode mixing, and providing signal via a sufficient number of the local extrema. To overcome these disadvantages, in this study, the Noise-Assisted Multivariate Empirical Mode Decomposition has been shown to classify the emotional state using electroencephalographic signals.
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