Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - Scopus: 2Improving Short-Term Memory Performance of Healthy Young Males Using Alpha Band Neurofeedback(International Society for Neurofeedback and Research, 2019-03-24) Gökşin, Barış; Yilmaz, Bulent; İçöz, KutayTo examine whether it was possible to improve short-term memory performance of healthy participants by increasing relative alpha band power (7–11.5 Hz) using neurofeedback, we first converted a commercial EEG device (EmotivEpoc) to a neurofeedback tool and collected data from 11 healthy Turkish male graduate students in five neurofeedback sessions. Before and after neurofeedback training, a memorization task using 10 English words and their Turkish meanings was applied to all participants. The results indicated that 6 out of 11 participants were able to enhance their relative alpha band power with respect to other bands in the frequency spectrum during neurofeedback sessions. Although there was no obvious improvement in their short-term memory performance, we may conclude that neurofeedback training was beneficial for the participants to focus their minds consciously. However, it is not easy to mention that neurofeedback training certainly improved or was irrelevant with short-term memory performance. This study is important in the sense that for such a focused group the use of a commercial, customized low-cost EEG device was shown to be feasible for neurofeedback training sessions. © 2019 Elsevier B.V., All rights reserved.Article Citation - Scopus: 5Hyperplastic and Tubular Polyp Classification Using Machine Learning and Feature Selection(Elsevier B.V., 2024) Doǧan, Refika Sultan; Akay, Ebru; Doǧan, Serkan; Yilmaz, BulentPurpose: The aim of this study is to develop an effective approach for differentiating between hyperplastic and tubular adenoma colon polyps, which is one of the most difficult tasks in colonoscopy procedures. The main research challenge is how to improve the classification of these polyp subtypes applying various focusing levels on the polyp images, data preprocessing approaches, and classification algorithms. Methods: This study employed 202 colonoscopy videos from a total of 201 patients, focusing on 59 videos containing hyperplastic and tubular adenoma polyps. Manually extract key frames and several feature extraction and classification techniques were applied. The influence of different datasets with various focuses as well as data preprocessing steps on the performance of classification was examined, and AUC values were calculated using ten classifiers. Results: The study discovered that the optimal dataset, data preprocessing method, and classification algorithm all had significant effects on classification results. The Random Forest model with the Recursive Feature Elimination (RFE) feature selection approach, for example, consistently outperformed other models and achieved the highest AUC value of 0.9067. In terms of accuracy, F1 score, recall, and AUC, the suggested model outperformed a gastroenterologist, nevertheless precision remained slightly lower. Conclusion: This study emphasizes the importance of dataset selection, data preprocessing, and feature selection in enhancing the classification of difficult colon polyp subtypes. The suggested model offers a promising model for the clinical differentiation of hyperplastic and tubular adenoma polyps, potentially improving diagnostic accuracy in gastroenterology. © 2024 Elsevier B.V., All rights reserved.Article Citation - WoS: 5Citation - Scopus: 6Genetic Variants in Genes Correlated to the PI3K/AKT Pathway: The Role of ARAP3, CDH5, KIF and RELN Primary Lymphedema(International Society of Lymphology, 2024-08-28) Dundar, Mehmet Sait; Belanová, I.; Bonetti, Gabriele; Gelanová, V.; Kozáčiková, R.; Vešelényiová, Dominika; Donato, Kevin; Michelini, S.Genetic anomalies affecting lymphatic development and function can lead to lymphatic dysfunction, which could manifest as lymphedema- Understanding the signaling pathways governing lymphatics function is crucial for developing targeted diagnostic and therapeutic interventions. This study aims to characterize genetic variants in genes involved in the PUKIAKT signaling pathway, which plays a critical role in lymphangiogenesis. 408 patients diagnosed with primary lymphedema were sequenced usinga next-generation sequencing (NGS) gene panel composed of 28 diagnostic genes and 71 candidate genes. The analysis revealed six variants in genes RFLN, ARAP3,CDHS and K1F11. Five of these variants have never been reported in the literature. All these genes have been correlated to lymphatic activity and are involved in the P13K/AKT pathway. As the P13K/AKT signaling pathway plays an essential role in lymphangiogenesis and lymphatic function, genetic variants in genes correlated to this pathway could lead to lymphedema. Our findings underscore the potential of the P13K/AKT pathway in lymphedema pathogenesis, supporting the role of RELN,ARAP3,CDH5,and KIF11 as diagnostic and therapeutic targets. © 2024 Elsevier B.V., All rights reserved.Article Citation - Scopus: 15An Effective Colorectal Polyp Classification for Histopathological Images Based on Supervised Contrastive Learning(Elsevier Ltd, 2024-04) Yengec-Tasdemir, Sena Busra; Aydin, Zafer; Akay, Ebru; Doǧan, Serkan; Yilmaz, BulentEarly detection of colon adenomatous polyps is pivotal in reducing colon cancer risk. In this context, accurately distinguishing between adenomatous polyp subtypes, especially tubular and tubulovillous, from hyperplastic variants is crucial. This study introduces a cutting-edge computer-aided diagnosis system optimized for this task. Our system employs advanced Supervised Contrastive learning to ensure precise classification of colon histopathology images. Significantly, we have integrated the Big Transfer model, which has gained prominence for its exemplary adaptability to visual tasks in medical imaging. Our novel approach discerns between in-class and out-of-class images, thereby elevating its discriminatory power for polyp subtypes. We validated our system using two datasets: a specially curated one and the publicly accessible UniToPatho dataset. The results reveal that our model markedly surpasses traditional deep convolutional neural networks, registering classification accuracies of 87.1% and 70.3% for the custom and UniToPatho datasets, respectively. Such results emphasize the transformative potential of our model in polyp classification endeavors. © 2024 Elsevier B.V., All rights reserved.