Browsing by Author "Aksebzeci, Bekir Hakan"

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Computer-Aided Classification of Breast Cancer Histopathological Images
(IEEE345 E 47TH ST, NEW YORK, NY 10017 USA, 2017) Aksebzeci, Bekir Hakan; Kayaalti, Omer; AGÜ, Yaşam ve Doğa Bilimleri Fakültesi, Biyomühendislik Bölümü; 01. Abdullah Gül University
Nowadays, one of the most common types of cancer is breast cancer. The early and accurate diagnosis of breast cancer has great importance in the treatment of the disease. In the diagnosis of breast cancer, histopathological analysis of cell and tissue specimens taken by biopsy is considered as the gold standard. Histopathological analysis is a tedious process that is highly dependent on the knowledge and experience of the pathologists. In this study; it is aimed to develop a computer-aided system that can reduce the workload of pathologists and help them in their diagnosis. An image set containing benign and malignant tumor images of breast cancer has been studied. To perform texture analysis on tumor images; first order statistics, Gabor and gray-level co-occurrence matrix (GLCM) feature extraction methods have been applied. Then, various classifiers were applied to the obtained feature matrices and their performances were compared. The highest classification accuracy was achieved 82.06% by Random Forests classifier with feature combination of Gabor and GLCM methods. The results presented here show that computer-assisted diagnosis of breast cancer is a promising field.
A Decision Support System for the Prediction of Mortality in Patients With Acute Kidney Injury Admitted in Intensive Care Unit
(Univ South Bohemia, 2020) Kayaalti, Selda; Kayaalti, Omer; Aksebzeci, Bekir Hakan; 01. Abdullah Gül University
Intensive care unit (ICU) is a very special unit of a hospital, where healthcare professionals provide treatment and, later, close followup to the patients. It is crucial to estimate mortality in ICU patients from many viewpoints. The purpose of this study is to classify the status of patients with acute kidney injury (AKI) in ICU as early mortality, late mortality, and survival by the application of Classification and Regression Trees (CART) algorithm to the patients' attributes such as blood urea nitrogen, creatinine, serum and urine neutrophil gelatinase-associated lipocalin (NGAL), alkaline phosphatase, lactate dehydrogenase (LDH), gamma-glutamyl transferase, laboratory electrolytes, blood gas, mean arterial pressure, central venous pressure and demographic details of patients. This study was conducted 50 patients with AKI who were followed up in the ICU. The study also aims to determine the significance of relationship between the attributes used in the prediction of mortality in CART and patients' status by employing the Kruskal-Wallis H test. The classification accuracy, sensitivity, and specificity of CART for the tested attributes for the prediction of early mortality, late mortality, and survival of patients were 90.00%, 83.33%, and 91.67%, respectively. The values of both urine NGAL and LDH on day 7 showed a considerable difference according to the patients' status after being examined by the Kruskal-Wallis H test.
Citation - WoS: 13
Design and Implementation of a Voice-Controlled Prosthetic Hand
(Tubitak Scientific & Technological Research Council Turkey, 2011) Asyali, Musa Hakan; Yilmaz, Mustafa; Tokmakci, Mahmut; Sedef, Kanber; Aksebzeci, Bekir Hakan; Mittal, Rohin; 01. Abdullah Gül University
Current hand prostheses are mostly driven by electromyography (EMG) signal, and existing experiments have proved that multichannel EMG signal controls are not suitable due to early fatigue problems and high effort, requirements to perform even simple activities. Therefore, in this study we present a new voice-controlled active hand prosthesis to perform several basic tasks. We first designed a novel multifingered prosthetic hand with the ability of picking up and releasing objects. The prosthetic hand employs 3 DC motors and gears to transfer motion to the linked parts of the fingers. We used flexible thin-film, resistive force sensors at the fingertips of the prosthetic hand to adjust the grip force at the fingers. The second part of the study involves the use of speech recognition to control the prosthetic hand. The control circuit that we designed consisted of an HM2007 speech recognition IC and a PIC microcontroller to drive the DC motors moving the fingers. We implemented both the prosthetic hand and its speech recognition-based control electronics. As of now, we have programmed the control hardware to recognize simple pick up and release operations and have successfully tested them. In a future study, we will include more voice commands for the operation of the hand, such as a realistic handshake, and improve the cosmetics of the hand in order to make it look more natural.
Citation - WoS: 28
Citation - Scopus: 31
Liver Fibrosis Staging Using CT Image Texture Analysis and Soft Computing
(Elsevier, 2014) Kayaalti, Omer; Aksebzeci, Bekir Hakan; Karahan, Ibrahim Okkes; Deniz, Kemal; Ozturk, Mehmet; Yilmaz, Bulent; Asyali, Musa Hakan; 01. Abdullah Gül University
Liver biopsy is considered to be the gold standard for analyzing chronic hepatitis and fibrosis; however, it is an invasive and expensive approach, which is also difficult to standardize. Medical imaging techniques such as ultrasonography, computed tomography (CT), and magnetic resonance imaging are non-invasive and helpful methods to interpret liver texture, and may be good alternatives to needle biopsy. Recently, instead of visual inspection of these images, computer-aided image analysis based approaches have become more popular. In this study, a non-invasive, low-cost and relatively accurate method was developed to determine liver fibrosis stage by analyzing some texture features of liver CT images. In this approach, some suitable regions of interests were selected on CT images and a comprehensive set of texture features were obtained from these regions using different methods, such as Gray Level Co-occurrence matrix (GLCM), Laws' method, Discrete Wavelet Transform (DWT), and Gabor filters. Afterwards, sequential floating forward selection and exhaustive search methods were used in various combinations for the selection of most discriminating features. Finally, those selected texture features were classified using two methods, namely, Support Vector Machines (SVM) and k-nearest neighbors (k-NN). The mean classification accuracy in pairwise group comparisons was approximately 95% for both classification methods using only 5 features. Also, performance of our approach in classifying liver fibrosis stage of subjects in the test set into 7 possible stages was investigated. In this case, both SVM and k-NN methods have returned relatively low classification accuracies. Our pairwise group classification results showed that DWT, Gabor, GLCM, and Laws' texture features were more successful than the others; as such features extracted from these methods were used in the feature fusion process. Fusing features from these better performing families further improved the classification performance. The results show that our approach can be used as a decision support system in especially pairwise fibrosis stage comparisons. (C) 2014 Elsevier B.V. All rights reserved.
Meme Kanseri Histopatoloji Görüntülerinde Evrişimsel Sinir Ağları Kullanarak Tümör Tespiti
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Şahbaz, Zeki; Aksebzeci, Bekir Hakan; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı; 01. Abdullah Gül University
Breast cancer is one of the most common cancer types among women worldwide. Early detection significantly increases the chances of survival and effective treatment, making advancements in diagnostic methodologies crucial. This study aims to improve the detection of tumor cells in breast cancer histopathology images using deep learning and image processing techniques. Significant modifications have been made to the hyperparameters, including the tumor bounding box size, batch size, optimization algorithms, learning rate, and weight decay. These changes focus on determining the best parameters of the Faster R-CNN model. A comprehensive analysis of different parameters was conducted using the Breast Cancer Histopathological Annotation and Diagnosis (BreCaHAD) dataset. The analysis identified the best settings for model performance, shows by improvements in precision, recall, and F-score. Our research contributes to the field of medical image analysis by identifying critical factors that affect the accuracy of tumor detection, contributing to the development of more accurate diagnostic tools.
Citation - WoS: 8
Citation - Scopus: 8
Meme Kanseri Histopatolojik Görüntülerinin Bilgisayar Destekli Sınıflandırılması
(Institute of Electrical and Electronics Engineers Inc., 2017) Aksebzeci, Bekir Hakan; Kayaaltı, Ömer; 01. Abdullah Gül University
Nowadays, one of the most common types of cancer is breast cancer. The early and accurate diagnosis of breast cancer has great importance in the treatment of the disease. In the diagnosis of breast cancer, histopathological analysis of cell and tissue specimens taken by biopsy is considered as the gold standard. Histopathological analysis is a tedious process that is highly dependent on the knowledge and experience of the pathologists. In this study; it is aimed to develop a computer-Aided system that can reduce the workload of pathologists and help them in their diagnosis. An image set containing benign and malignant tumor images of breast cancer has been studied. To perform texture analysis on tumor images; first order statistics, Gabor and gray-level co-occurrence matrix (GLCM) feature extraction methods have been applied. Then, various classifiers were applied to the obtained feature matrices and their performances were compared. The highest classification accuracy was achieved 82.06% by Random Forests classifier with feature combination of Gabor and GLCM methods. The results presented here show that computer-Assisted diagnosis of breast cancer is a promising field. © 2018 Elsevier B.V., All rights reserved.
Citation - WoS: 4
Relationship Between Neutrophil Gelatinase-Associated Lipocalin and Mortality in Acute Kidney Injury
(Galenos Yayincilik, 2018) Kayaalti, Selda; Kayaalti, Omer; Aksebzeci, Bekir Hakan; 01. Abdullah Gül University
Objective: Almost half of intensive care patients are affected by acute kidney injury (AKI). The purpose of this study is to determine parameters that can be used for predicting of early (within 28 days) and late (within 90 days) mortality in patients who are followed-up with AKI in intensive care units. Materials and Methods: In this study, a dataset that contains 50 patients with AKI in intensive care units was used. This dataset contains blood urea nitrogen, creatinine, plasma and urinary neutrophil gelatinase-associated hpocalin (NGAL), lactate dehydrogenase, alkaline phosphatase and gammaglutamyl transpeptidase values of patients who were admitted to intensive care for various reasons and who developed AKI on the days 1, 3 and 7. In addition to these values, laboratory results such as serum electrolytes on day 1, blood gas; vital signs such as mean arterial pressure, central venous pressure; and demographic data were also recorded. Data mining techniques were applied to determine correlation between all of these data and mortality. Results: The threshold level of urinary NGAL on day 7 was determined to be 69 ng/mL, and strong correlation was found between this threshold level and early mortality. Similarly, the threshold level of plasma NGAL on day 7 was determined to be 150 ng/mL, and this was highly correlated with early mortality. Besides, strong correlation was also found between the difference in the urinary NGAL levels on day 1 and 7, and early mortality. Conclusion: In this study, plasma and urinary NGAL levels were found to be closely related to early mortality in patients who were followed-up with AKI in intensive care units. On the other hand, any parameter associated with late mortality was not found.
Citation - Scopus: 13
Staging of the Liver Fibrosis From CT Images Using Texture Features
(2012) Kayaaltı, Ömer; Aksebzeci, Bekir Hakan; Karahan, Ökkeş Ibrahim; Deniz, Kemal; Öztürk, Menmet; Yilmaz, Bulent; Asyali, Musa Hakan; 0000-0003-2954-1217; 0000-0001-7476-8141; AGÜ; Aksebzeci, Bekir Hakan; Yilmaz, Bülent; Asyali, Musa Hakan; 01. Abdullah Gül University
Even though liver biopsy is critical for evaluating chronic hepatitis and fibrosis, it is an invasive, costly, and difficult to standardize approach. The developments in medical image processing and artificial intelligence methods have advanced the potential of using computer-aided diagnosis techniques in the classification of liver tissues. The aim of this study was to develop a non-invasive, cost-effective, and fast approach to specify fibrosis stage using the texture properties of computed tomography images of liver. Gray level co-occurrence matrix, discrete wavelet transform, and discrete Fourier transform were the image analysis tools in the feature extraction phase. Following dimension reduction of the texture features support vector machines and k-nearest neighbor methods were used in the classification phase of this study. Our results showed that our approach is feasible in fibrosis staging especially in pairwise stage comparisons with success rate of approximately 90%. © 2012 IEEE. © 2012 Elsevier B.V., All rights reserved.