Sürdürülebilir Kalkınma Amaçları

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• BildiriYalnız künye2025/r/gumushane/13955
  Tumor Classification via Subregion Combinations of Cortical L5PC6 Pyramidal Neurons

  Ozcan, Zubeyir; Bozkir, Ilknur Kayikcioglu; Kayikcioglu, Temel

  In this study, it was aimed to classify benign and malignant tumor samples by utilizing different subregions of a cortical L5PC6 pyramidal neuron. A spike-based learning method was applied to a real dataset, and a decision mechanism based on action potential generation was established by training a separate neuron for each class. During the training process, various types of synaptic connections and spike encoding schemes were evaluated and intensity coding yielded the highest performance for the pyramidal neuron. Regional analyses revealed that the basilar region particularly stood out in terms of classification success. The results demonstrate that the morphological and biophysical diversity of pyramidal neurons provides a significant advantage in classification tasks.

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• BildiriYalnız künye2023/r/gumushane/16778
  Shape of Masses in Contrast Mammography Images and First Order Statistical Features Classification Based on

  Kobya, Mehmet Emin; Ture, Hayati; Dogan, Ramazan Ozgur; Kayikcioglu, Temel; Teymur, Aykut; Kul, Sibel

  Mammography is a valuable tool in the diagnosis and staging of primary breast cancer. Contrast-enhanced mammography (CEM) combines an iodinated contrast agent with traditional mammography to increase diagnostic accuracy, especially in women with more dense parenchymal background patterns. In this study, a Decision Support System (DSS) is proposed that classifies masses using radiomic features obtained from masks of benign and malignant masses in images obtained with the CEM technique. An open-access database containing 2006 CEM images was used in this study. Radiomic features obtained from these images were used to identify differences between benign and malignant masses. The DSS uses these features to classify masses using a decision tree algorithm. This study highlights the importance of the use of iodinated contrast agents to improve diagnostic accuracy in women with more dense parenchymal background patterns. Additionally, it emphasizes the potential benefits of a DSS, in combination with radiomic features, in the diagnosis and staging of breast cancer. In conclusion, this study demonstrates that the use of radiomic features in mammography images obtained with the CEM technique is an effective way to classify benign and malignant masses.The use of this technology could be an important tool in the early diagnosis and accurate classification of breast cancer.

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• BildiriYalnız künye2023/r/gumushane/12734
  An Efficient 1D Autoencoder-Based Approach for R-Peaks Detection in Electrocardiogram Signals

  Dogan, Ramazan Ozgur; Dogan, Hulya; Kayikcioglu, Temel

  Electrocardiogram (ECG) signal which is composition of multiple segments such as P-wave, QRS complex and T-wave plays a crucial role in evaluating human heart cardiac diagnosis. For an analysis of cardiac diagnosis, it is required that clinicians scan the ECG signal for R-peaks (the highest peak of the QRS complex) detection, which relies on their expertise and takes enormous time. In order to provide more realistic treatment of cardiovascular diseases in the ECG signal with noises and different characteristics, computer-based R-peaks detection approaches have been actively developed in the research article for many years and generally compose of two major operations; preprocessing and decision. Contrary to previous approaches which require high computational costs and complexity, appropriate initial parameters, a novel CNN based approach is presented for R-peaks detection in this paper. Both qualitative and quantitative analysis are performed in the real ECG signal taken from typical MIT-BIH arrhythmia database. In order to validate the success of the recommended approach, total 18 different R-peaks detection approaches reported in the literature are compared by utilizing performance evaluation metrics. Average values of sensitivity, positive predictivity, error rate, accuracy and f-score are computed as 99,96%, 99,95%, 0,09%, 99,90% and 99,95% respectively, which are the best values among well-established studies for R-peaks detection. It is demonstrated with qualitative and quantitative results that better performance is achieved by our suggested approach.

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• BildiriYalnız künye2022/r/gumushane/15989
  Segmentation of Pectoral Muscle Region in MLO Mammography Images by Backboned U-Net

  Dogan, Ramazan Ozgur; Türe, Hayati; Kayikçioglu, Temel

  The pectoral muscle region on MLO mammography images appears prominently similar to suspicious areas. For this reason, Computer-Aided Detection (CAD) systems remove this region to reduce false-positive rates in the mass detection process. In some cases, the pectoral muscle region is exposed to distortions due to the superposition effects caused by the mammography technique. As a result, segmentation error rates of the pectoral muscle region, whose characteristic features are deteriorated, appear. In this study, a method to identify impaired pectoral muscle regions with MobileNetV2 backboned U-Net Deep Learning method is proposed. The proposed method was tested on 84 and 201 mammography images taken from both MIAS and InBreast databases and segmented with 1.81% and 1.92% false-negative (FN) and 0.25% and 0.37% false positive (FP) rates, respectively. Particularly for distorted pectoral muscle regions, the proposed method has been shown to outperform some pioneering studies in this area.

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• MakaleAçık Erişim2021/r/gumushane/16899
  Eeg sinyallerini kullanarak 2B ve 3B hibrit bir videonun ayrıntılı kanal/lob analizi

  Melek, Negin; Melek, Mesut; Kayikcioglu, Temel

  İnsan gözünün yapısına göre 2 Boyutlu (2B) ve 3B parçalardan oluşan video analizinde ani geçiş ve sabit durumların değerlendirilmesi önemlidir. Bu çalışmada, insan beyin sinyallerinin güç spektrum yoğunluğu (GSY), 2B ve 3B hibrit video izleme sonucunda analiz için dikkate alınmıştır. İnsanların yorulunca derinlik algısının kaybettiğini daha önceki çalışmalarımızda iddia etmiştik. Bu çalışmada, rastgele 2B ve 3B parçalardan oluşan, tek akışlı bir anaglif video sağlanmıştır. 2B ve 3B hibrit video çalışmasında, beyin sinyal analizinde kısa zamanlı Fourier dönüşümüne (KZFD) dayalı GSY ve spektrogram adı verilen görsel temsil kullanılmıştır. Tüm EEG frekans bantları test edildikten sonra, spektrogram çizelgesindeki 2B ve 3B parçaların karşılaştırılmasında GSY farkı dikkate alınarak, delta bandı baskın bant olarak seçilmiştir. Bu banttan çıkarılan öznitelikler iki popüler sınıflandırıcı tarafından sınıflandırılmıştır. Bunlar destek vektör makinesi (DVM) ve doğrusal ayırma analizi (DAA) algoritmalarıdır. Sonuç olarak, frontal ve temporal loblar, 2B ve 3B geçişlerin sınıflandırılmasında diğer loblara göre daha iyi sonuçlar göstermektedir. Öznitelik çıkarma yöntemi olarak kullanılan istatistiksel fonksiyonlar ve Hjorth parametreleri sonucunda DVM ve DAA algoritmaları için sınıflandırma başarısı sırasıyla %68 ve %79 olarak hesaplanmıştır.

  İncele
• BildiriYalnız künye2021/r/gumushane/16786
  BroomyCell: An Introductory Educational Software for Neuroscience

  Ozcan, Zubeyir; Yildirim, Omer; Kayikcioglu, Temel

  Nerve cells are the basic building blocks of the nervous system. Therefore, the first step in neuroscience education is the introduction of nerve cells. However, both the complexity of nerve cells and the difficulty of their experimental examination make it difficult to test nerve cells practically. Therefore, simulation of nerve cells is found to be significantly effective. The difficulty of examining this type of cells experimentally shows its effect on neuroscience education. This brings to the fore the necessity of virtual laboratory environments designed for use in education. In this study, BroomyCell, which is designed by us as an introductory educational software in neuroscience education, was introduced. With this desktop application, it is aimed that potential neuroscientists learn their fields experimentally and without the need for a physical laboratory.

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• MakaleYalnız künye2021/r/gumushane/14926
  Polydimethylsiloxane-based capacitive motion sensor and its read-out circuit

  Yildirim, Omer; Bozyel, Ibrahim; Saka, Kubra; Kayikcioglu, Temel; Gokcen, Dincer

  A capacitive motion sensor that can detect human walking and motions up to a certain distance was designed using polydimethylsiloxane (PDMS) as the primary sensor material. A read-out circuit was designed to detect capacitive coupling with the sensor material caused by the motion in the surrounding. The read-out circuit consists of sensing, filtering, and amplifying stages. A high-pass filter was used to avoid DC components, and to eliminate adverse impacts of 50Hz noise in the sensing signal, a sixth-order low-pass filter consisting of active Sallen–Key and passive RC filters was utilized. The signals obtained from the complete read-out circuit revealed that the sensor system could accurately detect human movements up to 2 m away. Also, different signal patterns were obtained from the output of the sensor system for various human motions.

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• MakaleYalnız künye2021/r/gumushane/14912
  Brain-computer interface systems for smart homes-a review study

  Maleki, Masoud; Manshouri, Negin; Kayikcioglu, Temel

  Brain-computer Interface (BCI) systems, usually using signals taken from users' brain through electroencephalography (EEG), control various devices around and provide the user's com-mand by interacting. Improving the quality of life of people with disabilities is the main goal of BCI systems. The importance of BCI-based smart home systems is further increasing as a smart home system directly affects the life of a disabled individual. On the other hand, few BCI systems can be run directly using smart home systems. The importance of the BCI-based smart home and the few existing systems require more work in this vital field. In addition, no reviews have described BCI systems in a smart home. In this study, we reviewed all the papers on BCI-based smart home systems published in the last 6 years. These studies investigated and evaluated BCI systems from nine different perspectives. In addition, all studies were examined in terms of signal processing methods. Finally, the problems and challenges of these systems were discussed and new solutions were pro-posed.

  İncele
• MakaleAçık Erişim2021/r/gumushane/14780
  A Two-Phase Approach using Mask R-CNN and 3D U-Net for High-Accuracy Automatic Segmentation of Pancreas in CT Imaging

  Dogan, Ramazan Ozgur; Dogan, Hulya; Bayrak, Coskun; Kayikcioglu, Temel

  Background and objective: The size, shape, and position of the pancreas are affected by the patient characteristics such as age, sex, adiposity. Owing to more complex anatomical structures (size, shape, and position) of the pancreas, specialists have some difficulties in the analysis of pancreatic diseases (diabetes, pancreatic cancer, pancreatitis). Therefore, the treatment of the disease requires enormous time and depends on the experience of specialists. In order to decrease the rate of pancreatic disease deaths and to assist the specialist in the analysis of pancreatic diseases, automatic pancreas segmentation techniques have been actively developed in the research article for many years. Methods: Although the rapid growth of deep learning and proving satisfactory performance in many medical image processing and computer vision applications, the maximum Dice Similarity Coefficients (DSC) value of these techniques related to automatic pancreas segmentation is only around 85% due to complex structure of the pancreas and other factors. Contrary to previous techniques which are required significantly higher computational power and memory, this paper suggests a novel two-phase approach for high-accuracy automatic pancreas segmentation in computed tomography (CT) imaging. The proposed approach consists of two phases; (1) Pancreas Localization, where the rough pancreas position is detected on the 2D CT slice by adopting Mask R-CNN model, (2) Pancreas Segmentation, where the segmented pancreas region is produced by refining the candidate pancreas region with 3D U-Net on the 2D sub-CT slices generated in the first phase. Both qualitative and quantitative assessments of the proposed approach are performed on the NIH data set. Results: In order to evaluate the achievement of the recommended approach, a total of 16 different automatic pancreas segmentation techniques reported in the literature are compared by utilizing performance assessment procedures which are Dice Similarity Coefficient (DSC), Jaccard Index (JI), Precision (PRE), Recall (REC), Pixel Accuracy (ACC), Specificity (SPE), Receiver Operating Characteristics (ROC) and Area under ROC curve (AUC). The average values of DSC, JI, REC and ACC are computed as 86.15%, 75.93%, 86.27%, 86.27% and 99.95% respectively, which are the best values among well-established studies for automatic pancreas segmentation. Conclusion: It is demonstrated with qualitative and quantitative results that our suggested two-phase approach creates more favorable results than other existing approaches.

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  İncele
• MakaleYayıncıda açık erişim2021/r/gumushane/13285
  An automatic EEG-based sleep staging system with introducing NAoSP and NAoGP as new metrics for sleep staging systems

  Melek, Mesut; Manshouri, Negin; Kayikcioglu, Temel

  Different biological signals are recorded in sleep labs during sleep for the diagnosis and treatment of human sleep problems. Classification of sleep stages with electroencephalography (EEG) is preferred to other biological signals due to its advantages such as providing clinical information, cost-effectiveness, comfort, and ease of use. The evaluation of EEG signals taken during sleep by clinicians is a tiring, time-consuming, and error-prone method. Therefore, it is clinically mandatory to determine sleep stages by using software-supported systems. Like all classification problems, the accuracy rate is used to compare the performance of studies in this domain, but this metric can be accurate when the number of observations is equal in classes. However, since there is not an equal number of observations in sleep stages, this metric is insufficient in the evaluation of such systems. For this purpose, in recent years, Cohen’s kappa coefficient and even the sensitivity of NREM1 have been used for comparing the performance of these systems. Still, none of them examine the system from all dimensions. Therefore, in this study, two new metrics based on the polygon area metric, called the normalized area of sensitivity polygon and normalized area of the general polygon, are proposed for the performance evaluation of sleep staging systems. In addition, a new sleep staging system is introduced using the applications offered by the MATLAB program. The existing systems discussed in the literature were examined with the proposed metrics, and the best systems were compared with the proposed sleep staging system. According to the results, the proposed system excels in comparison with the most advanced machine learning methods. The single-channel method introduced based on the proposed metrics can be used for robust and reliable sleep stage classification from all dimensions required for real-time applications.

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