Browsing by Author "Aksebzeci, Bekir Hakan"

Now showing 1 - 5 of 5

Akut Böbrek Hasarında Nötrofil Jelatinaz İlişkili Lipokalin ile Mortalite İlişkisi
(GALENOS YAYINCILIK, ERKAN MOR, MOLLA GURANI CAD 21-1, FINDIKZADE, ISTANBUL 34093, TURKEY, 2018) Aksebzeci, Bekir Hakan; Kayaaltı, Seda; Kayaaltı, Ömer; AGÜ, Yaşam ve Doğa Bilimleri Fakültesi, Biyomühendislik Bölümü;
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.
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
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, FAC HEALTH & SOCIAL STUD, JIROVCOVA, CESKA BUDEJOVICE, 370 04, CZECH REPUBLIC, 2020) Kayaalti, Selda; Kayaalti, Omer; Aksebzeci, Bekir Hakan; 0000-0001-6711-2363; 0000-0002-1630-1241; AGÜ, Yaşam ve Doğa Bilimleri Fakültesi, Biyomühendislik Bölümü
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.
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; Kara, Sadik; Asyali, Musa Hakan; 0000-0001-7476-8141; 0000-0003-2954-1217; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Yilmaz, Bulent; Aksebzeci, Bekir Hakan
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.
Staging of the liver fibrosis from CT images using texture features
(2012) Kayaalti, Ömer; Aksebzeci, Bekir Hakan; Karahan, Ibrahim Ö.; Deniz, Kemal; Öztürk, Menmet; Yilmaz, Bülent; Kara, Sadik; Asyali, Musa Hakan; 0000-0003-2954-1217; 0000-0001-7476-8141; AGÜ; Aksebzeci, Bekir Hakan; Yilmaz, Bülent; Asyali, Musa Hakan
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%.