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Öğe Classification of DNA damages on segmented comet assay images using convolutional neural network(Elsevier Ireland Ltd, 2020) Atila, Umit; Baydilli, Yusuf Yargi; Sehirli, Eftal; Turan, Muhammed KamilBackground and Objective: Identification and quantification of DNA damage is a very significant subject in biomedical research area which still needs more robust and effective methods. One of the cheapest, easy to use and most successful method for DNA damage analyses is comet assay. In this study, performance of Convolutional Neural Network was examined on quantification of DNA damage using comet assay images and was compared to other methods in the literature. Methods: 796 single comet grayscale images with 170 x 170 resolution labeled by an expert and classified into 4 classes each having approximately 200 samples as G0 (healthy), G1 (poorly defective), G2 (defective) and G3 (very defective) were utilized. 120 samples were used as test dataset and the rest were used in data augmentation process to achieve better performance with training of Convolutional Neural Network. The augmented data having a total of 9995 images belonging to four classes were used as network training data set. Results: The proposed model, which was not dependent to pre-processing parameters of image processing for DNA damage classification, was able to classify comet images into 4 classes with an overall accuracy rate of 96.1%. Conclusions: This paper primarily focuses on features and usage of Convolutional Neural Network as a novel method to classify comet objects on segmented comet assay images. (C) 2019 Elsevier B.V. All rights reserved.Öğe Comparison of pirfenidone and corticosteroid treatments at the COVID-19 pneumonia with the guide of artificial intelligence supported thoracic computed tomography(Wiley, 2021) Acat, Murat; Gulhan, Pinar Yildiz; Oner, Serkan; Turan, Muhammed KamilAim We aimed to investigate the effect of short-term pirfenidone treatment on prolonged COVID-19 pneumonia. Method Hospital files of patients hospitalised with a diagnosis of critical COVID-19 pneumonia from November 2020 to March 2021 were retrospectively reviewed. Chest computed tomography images taken both before treatment and 2 months after treatment, demographic characteristics and laboratory parameters of patients receiving pirfenidone + methylprednisolone (n = 13) and only methylprednisolones (n = 9) were recorded. Pulmonary function tests were performed after the second month of the treatment. CT involvement rates were determined by machine learning. Results A total of 22 patients, 13 of whom (59.1%) were using methylprednisolone + pirfenidone and 9 of whom (40.9%) were using only methylprednisolone were included. When the blood gas parameters and pulmonary function tests of the patients were compared at the end of the second month, it was found that the FEV1, FEV1%, FVC and FVC% values were statistically significantly higher in the methylprednisolone + pirfenidone group compared with the methylprednisolone group (P = .025, P = .012, P = .026 and P = .017, respectively). When the rates of change in CT scans at diagnosis and second month of treatment were examined, it was found that the involvement rates in the methylprednisolone + pirfenidone group were statistically significantly decreased (P < .001). Conclusion Antifibrotic agents can reduce fibrosis that may develop in the future. These can also help dose reduction and/or non-use strategy for methylprednisolone therapy, which has many side effects. Further large series and randomised controlled studies are needed on this subject.Öğe Estimation of gender by using decision tree, a machine learning algorithm, with patellar measurements obtained from mdct images(2021) Öner, Serkan; Turan, Muhammed Kamil; Öner, ZülalAim: The present study aimed to analyze whether gender could be determined with the decision tree (DT) method, a machine learning algorithm, based on patellar multidetector computed tomography (MDCT) image measurements. Material and Methods: The study was conducted on 219 male and 131 female MDCT images. The patellar anteroposterior (Ap), craniocaudal (Cc), transverse (Trv) length and volume (Vol), adjusted on the orthogonal plane by the radiologist, were calculated. In patellar length measurements, initially linear discriminant outliers were detected to clear the data for gender prediction. Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), F1-Score (F1) and Matthew’s Correlation Coefficient (Mcc) criteria were taken as the performance criteria for DT. Results: It was determined that male Ap, Trv, Cc, and Vol values were higher when compared to the female values and there was a significant difference between these values based on gender (pAp, Trv, Cc, Vol = 0.000). Using the above-mentioned measurements, it was calculated that the prediction rate for male individuals was 98.2% and for female individuals, it was 98.4%. Conclusion: DT analysis based on patella morphometry provided a simple, adequate and highly accurate approach for gender estimation. Furthermore, it was determined that it would provide an advantage for researchers in gender prediction using only branching and cut-off values on the tree structure without the need to use a computer.Öğe Gender prediction with parameters obtained from pelvis computed tomography images and decision tree algorithm(2021) Seçgin, Yusuf; Öner, Zülal; Turan, Muhammed Kamil; Öner, SerkanGender prediction is among the most critical topics in forensic medicine and anthropology since it is the basis of identity (height, weight, ancestry, age). Today, osteometry which is a low-cost, easily accessible method that requires no expertise is preferred when compared to DNA technology, which has several disadvantages such as high cost, accessibility, laboratory facilities, and expert personnel requirements. The Computed Tomography (CT) method, which is little affected by orientation and provides reconstruction opportunities, was selected instead of traditional methods for osteometry. This study aims to predict high and accurate gender with the Decision Tree (DT) algorithms used in the field of health recently. In the present study, CT images of 300 individuals (150 females, 150 males) without a pathology on the pelvic skeleton and between the ages of 25 and 50 were transformed into orthogonal form, landmarks were placed on promontorium, sacroiliac joint, iliac crest, terminal line, anterior superior iliac spine, anterior inferior iliac spine, greater trochanter, obturator foramen, lesser trochanter, femoral head, femoral neck, the body of femur, ischial tuberosity, acetabulum, and pubic symphysis, and the coordinates of these landmarks were determined. Then, parameters such as angle and length were obtained with various combinations. These parameters were analyzed with the DT algorithm.The analysis conducted with the DT algorithm revealed that accuracy (Acc) was 0.93, sensitivity was 0.95, specificity was 0.90, and the Matthews correlation coefficient was 0.86 for the pelvic skeleton. It was observed that the accuracy was quite high and more realistic when determined with the DT algorithm. In conclusion, the DT algorithm with multiple parameters and samples on pelvic CT images could improve the Acc of gender prediction.Öğe Gender prediction with the parameters obtained from pelvis computed tomography images and machine learning algorithms(Wolters Kluwer Medknow Publications, 2022) Secgin, Yusuf; Oner, Zulal; Turan, Muhammed Kamil; Oner, SerkanIntroduction: In the skeletal system, the most dimorphic bones employed for postmortem gender prediction include the bones in the pelvic skeleton. Bone measurements are usually conducted with cadaver bones. Computed tomography (CT) is an increasingly popular method due to its ease of use, reconstruction opportunities, and lower impact of age bias and provides a modern data source. Even when parameters obtained with different or same bones are missing, machine learning (ML) algorithms allow the use of statistical methods to predict gender. This study was carried out in order to obtain high accuracy in estimating gender with the pelvis skeleton by integrating ML algorithms, which are used extensively in the field of engineering, in the field of health. Material and Methods: In the present study, pelvic CT images of 300 healthy individuals (150 females, 150 males) between the ages of 25 and 50 (the mean female age = 40, the mean male age = 37) were transformed into orthogonal images, and landmarks were placed on promontory, iliac crest, sacroiliac joint, anterior superior iliac spine, anterior inferior iliac spine, terminal line, obturator foramen, greater trochanter, lesser trochanter, femoral head, femoral neck, body of femur, ischial tuberosity, acetabulum, and pubic symphysis, and coordinates of these regions were obtained. Four groups were formed based on various angle and length combinations obtained from these coordinates. These four groups were analyzed with ML algorithms such as Logistic Regression, Linear Discriminant Analysis (LDA), Random Forest, Extra Trees Classifier, and ADA Boost Classifier. Results: In the analysis, it was determined that the highest accuracy was 0.96 (sensitivity 0.95, specificity 0.97, Matthew's Correlation Coefficient 0.93) with LDA. Discussion and Conclusion: The use of length and angle measurements obtained from the pelvis showed that the LDA model was effective in estimating gender.Öğe Identification of column edges of DNA fragments by using K-means clustering and mean algorithm on lane histograms of DNA agarose gel electrophoresis images(Spie-Int Soc Optical Engineering, 2015) Turan, Muhammed Kamil; Sehirli, Eftal; Elen, Abdullah; Karas, Ismail RakipGel electrophoresis (GE) is one of the most used method to separate DNA, RNA, protein molecules according to size, weight and quantity parameters in many areas such as genetics, molecular biology, biochemistry, microbiology. The main way to separate each molecule is to find borders of each molecule fragment. This paper presents a software application that show columns edges of DNA fragments in 3 steps. In the first step the application obtains lane histograms of agarose gel electrophoresis images by doing projection based on x-axis. In the second step, it utilizes k-means clustering algorithm to classify point values of lane histogram such as left side values, right side values and undesired values. In the third step, column edges of DNA fragments is shown by using mean algorithm and mathematical processes to separate DNA fragments from the background in a fully automated way. In addition to this, the application presents locations of DNA fragments and how many DNA fragments exist on images captured by a scientific camera.Öğe Investigation of associations between apolipoprotein A5 and C3 gene polymorphisms with plasma triglyceride and lipid levels(Assoc Medica Brasileira, 2023) Taskin, Emre; Bagci, Hasan; Turan, Muhammed KamilOBJECTIVE: The aim of this study was to determine frequency and associations between APOA5 c.56C>G, -1131T>C, c.553G>T, and APOC3-482C>T and SstI gene polymorphisms with hypertriglyceridemia. METHODS: Under a case-control study model, 135 hypertriglyceridemic and 178 normotriglyceridemic control participants were recruited. Polymerase chain reaction and restriction fragment length polymorphism methods were utilized for genotyping. Statistical calculations were performed by comparing allele and genotype frequencies between groups. Clinical characteristics were compared between groups and intra-group genotypes. RESULTS:APOC3 gene -482C>T and SstI polymorphic genotypes and allele frequencies were significantly higher in hypertriglyceridemic group (genotype frequencies, p=0.035, p=0.028, respectively). Regression analysis under unadjusted model confirmed that APOC3-482C>T and SstI polymorphisms were significantly contributing to have hypertriglyceridemia (p=0.02, odds ratio [OR]=1.831 (95% confidence interval [CI] 1.095-3.060); p=0.04, OR=1.812 (1.031-3.183), respectively).APOA5 c.56C>G was in complete linkage disequilibrium with APOA5 c.553G>T polymorphism (D'=1). CONCLUSION: For the first time in a population sample from Turkey, among the five polymorphisms of APOA5 and APOC3 genes investigated,APOC3-482C>T and SstI polymorphisms were associated with elevated serum TG levels, while APOA5 c.56C>G, -1131T>C, and c.553G>T polymorphisms were not.Öğe A new approach for fully automated segmentation of peripheral blood smears(Inst Advanced Science Extension, 2018) Elen, Abdullah; Turan, Muhammed KamilPeripheral blood smear is microscopically examining technique for blood samples from patients by painting special dyes in clinic laboratories. Blood diseases can be diagnosed by examining morphology, numbers and percentages of leukocyte, erythrocyte and thrombocyte cells in blood samples. However, this method is a considerably time-consuming process and requires an evaluation performed by a hematology specialist. It is not often provided a definitive assessment due to the expert's clinical experience and judgment during review. Although there are considerable studies about the segmentation of blood smear images in the literature, there is no method to segment all blood cells. In this study, a new segmentation algorithm is proposed, which automatically extracts leukocyte, erythrocyte and thrombocyte cells from peripheral blood smear images. Purpose of this study here is to make highly accurate and complete blood count. The algorithm treats each image as a universal set and represents each object in the image as a subset as a result of the applied operations. In the developed method, leukocytes and thrombocytes achieve better success than other studies. However, it has been observed that the average success rate of stacked erythrocytes decreases. Statistical tests of the developed method were performed using 200 blood smear images in experimental studies. According to the obtained results, it is seen that high accuracy (leukocyte 99.86%, thrombocyte 98.4%, erythrocyte 93.4%) and precision (leukocyte 94.77%, thrombocyte 90.14%, erythrocyte 95.88%) were achieved in all three blood cells. (C) 2017 The Authors. Published by IASE.Öğe A new approach for measuring the wetting angles of lead-free solder alloys from digital images(Elsevier - Division Reed Elsevier India Pvt Ltd, 2022) Sehirli, Eftal; Erer, Ahmet Mustafa; Turan, Muhammed KamilThis study aims to detect quaternary lead-free solder alloys and measure their dynamic wetting angles with a new approach. The detection of the prepared alloy drop and measurement of its wetting angle are automatically performed by the developed application. A video obtained after experimental studies is filtered over its frames and enhanced a better form in the preprocessing stage. The peak of the half alloy drop is detected by obtaining lane histogram and its global minimum point is found in the segmentation stage. Left and right corner points in contact with the ground are detected by tracking pixel intensity values with the help of the global minimum point. The median point for both left half alloy drop and right half alloy drop is calculated. Thus, circles passing through three points for the left half and right half of the alloy drop are obtained to measure theta(left), theta(right) and theta(mean) wetting angles. In the postprocessing stage, these angles are measured at 0th, 5th, 10th, 15th, 30th, 60th, 90th, 120th, 150th, and 300th seconds after the alloy drop contacted the ground. Besides, measurement of these wetting angles is performed at each second. The obtained results showed that the graph of theta(mean) versus time is an exponential decay graph. This result proves that the alloy drop spreads over the ground as time passes and the value of theta(mean) decreases and is consistent with the observation made during the experiment. (c) 2022 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Öğe A novel method for segmentation of qrs complex on ecg signals and classification of cardiovascular diseases via a hybrid model based on machine learning(2021) Sehırlı, Eftal; Turan, Muhammed KamilAutomated-detecting intelligent programs and methods are developing to find out diseases in medicine in recent years.Developing new methods and improving existing ones are currently ongoing research. One of the most important health problems is heartdiseases for all people in the world. Electrocardiography (ECG) is a diagnosis tool that gives substantially functional information aboutheart and cardiac system. In this work, it is primarily aimed at developing an intelligent system based on ECG signal processing, analysis,and classification via a hybrid machine learning model. This work uses 837 ECG signal fragments that includes 7 different classes sharedin MIT-BIH Arrhythmia database for one lead. The ECG signals are applied on a preprocessing to smooth signals and correct baselines.Q, R and S waves (QRS) complex on ECG signals are segmented based on k-means clustering and tracking local extrema points. Featureextraction and selection are then performed, and a dataset is created by calculating measurement parameters for each QRS points separately.Training sets and test sets based on 8-fold cross validation are generated. A hybrid model based on machine learning models includingdecision tree (DT), k-nearest neighbor (KNN), random forest (RF), naïve bayes (NB), linear discriminant analysis (LDA), support vectormachines (SVM) and quadratic discriminant analysis (QDA) is developed to classify cardiovascular diseases (CVD) into 7 different classessuch as normal sinus rhythm (NSR), atrial premature beat (APB), atrial fibrillation (AFIB), premature ventricular contraction (PVC),ventricular bigeminy (VB), left bundle branch block beat (LBBBB) and right bundle branch block beat (RBBBB). Sensitivity, specificity,accuracy, and Matthews correlation coefficient (MCC) of detection of QRS complex are obtained respectively as 94.75%, 95.96%, 95.57%and 0.90. Sensitivity, specificity, accuracy and MCC of classification of CVD classes are obtained respectively as 92.33%, 92.50%,92.41%, 0.85.Öğe A novel method to identify and grade DNA damage on comet images(Elsevier Ireland Ltd, 2017) Turan, Muhammed Kamil; Sehirli, EftalBackground and objective: In recent years, development of software programs in medicine field has proceeded in a rapid manner. Comet assay is one of the research methods in medicine field that displays whether DNA has damage. In this study, it is aimed to share experience of dynamic time warping method and decision tree to decide whether DNA has damage and grade DNA damage by means of the software program. Methods: The application analyzes manually extracted RGB single comet images whose centers are manually marked. The application performs pixel profile analysis at the directions of vertical and horizontal based on the center of comet. The bidirectional pixel profile results are used as inputs of dynamic time warping. Four novel and one conventional measurement parameters some of which are calculated by dynamic time warping are given to decision tree. Results: The decision tree identifies whether DNA has damage and grades DNA damage categorizing four damage levels with accuracy of 99.03% using only two of five measurement parameters. Conclusions: This paper mainly focuses on features and usage of dynamic time warping and decision tree as a novel method to identify and grade DNA damage on comet images. (C) 2017 Elsevier B.V. All rights reserved.Öğe On fractal cubic network graphs(Elsevier, 2025-03) Altintas Tankul, Ayse Nur; Selcuk, Burhan; Turan, Muhammed KamilThe fractal cubic network graphs (FCNG), previously studied by Karci and Selcuk (2015), are reviewed in this paper. First, general information about FCNG is provided, and new topological properties of FCNG are presented. Simulations of the topological properties of FCNG, hypercube, and 2D square meshes have been performed, and the results are introduced. Secondly, a strategy for the routing problem for FCNG is presented. A new strategy for the routing path of FCNG is presented and explained with an example, and a recursive algorithm using this strategy is presented. Thirdly, a strategy for the shortest path problem for FCNG with a similar routing strategy is also presented, and a recursive algorithm for this strategy is given. An algorithm for mapping network nodes on a 2D plane and an algorithm for computing the minimum distance connection point between fractals used to construct the shortest path are also provided. These algorithms are illustrated with an example. The running times of the algorithms are also calculated.Öğe A proposal of a hybrid model to predict the secondary protein structures based on amino acid sequences(2020) Turan, Muhammed Kamil; Bagcı, HasanAim: Predicting the secondary structure of proteins based on amino acid sequences is one of the most significant issues inbioinformatics that requires clarification. A high accuracy in determining the secondary structure is a key to programmaticallyuncover 3D structure of proteins and for individual drug applications of programmable proteins. The success rates in predicting thesecondary structures (Q3 score) were around 0.60 when relevant research was initiated and now the rates have reached to the limitof 0.80.Material and Methods: In this study, the secondary structure was predicted through 3-state (Helix, Strand and Turn). Artificial neuralnetworks and machine learning algorithms were used as a hybrid model and a framework was developed. The probability of thepaired presence of amino acids in sequences was used in digitizing amino acid sequences. Calculations were completed separatelyfor each secondary structural element and the cascade mean filter was used as a threshold method to clarify the differences. Thegenerated matrices were used to digitize the protein sequences. Secondary structure was predicted through the Helix-Strand, HelixTurn, Strand-Turn, and subsequently, a final decision as Helix, Strand and Turn was reached via machine learning models.Results: It was determined that the success rates in the dual estimation of secondary structural elements were 0.797 for helixstrand, 0.848 for helix-turn and 0.829 for strand-turn. The average success rate for paired estimation of secondary structuralelements was calculated as 0.824. In the proposed model, accuracy was calculated as 0.742 for Helix, 0.703 for Strand and 0.880for Turn. Q3 score was obtained as 0.775.Öğe Real-Time Protozoa Detection from Microscopic Imaging Using YOLOv4 Algorithm(Mdpi, 2024) Kahraman, Idris; Karas, Ismail Rakip; Turan, Muhammed KamilProtozoa detection and classification from freshwaters and microscopic imaging are critical components in environmental monitoring, parasitology, science, biological processes, and scientific research. Bacterial and parasitic contamination of water plays an important role in society health. Conventional methods often rely on manual identification, resulting in time-consuming analyses and limited scalability. In this study, we propose a real-time protozoa detection framework using the YOLOv4 algorithm, a state-of-the-art deep learning model known for its exceptional speed and accuracy. Our dataset consists of objects of the protozoa species, such as Bdelloid Rotifera, Stylonychia Pustulata, Paramecium, Hypotrich Ciliate, Colpoda, Lepocinclis Acus, and Clathrulina Elegans, which are in freshwaters and have different shapes, sizes, and movements. One of the major properties of our work is to create a dataset by forming different cultures from various water sources like rainwater and puddles. Our network architecture is carefully tailored to optimize the detection of protozoa, ensuring precise localization and classification of individual organisms. To validate our approach, extensive experiments are conducted using real-world microscopic image datasets. The results demonstrate that the YOLOv4-based model achieves outstanding detection accuracy and significantly outperforms traditional methods in terms of speed and precision. The real-time capabilities of our framework enable rapid analysis of large-scale datasets, making it highly suitable for dynamic environments and time-sensitive applications. Furthermore, we introduce a user-friendly interface that allows researchers and environmental professionals to effortlessly deploy our YOLOv4-based protozoa detection tool. We conducted f1-score 0.95, precision 0.92, sensitivity 0.98, and mAP 0.9752 as evaluating metrics. The proposed model achieved 97% accuracy. After reaching high efficiency, a desktop application was developed to provide testing of the model. The proposed framework's speed and accuracy have significant implications for various fields, ranging from a support tool for paramesiology/parasitology studies to water quality assessments, offering a powerful tool to enhance our understanding and preservation of ecosystems.Öğe Sekazu: an integrated solution tool for gender determination based on machine learning models(2021) Turan, Muhammed Kamil; Sehırlı, Eftal; Öner, Zülal; Öner, SerkanGender determination is the first stage of identification used in forensic investigation, anthropology, archeology, and bioarchaeology, which helps accelerate the process of narrowing possible matches in a medical-legal context. Without DNA analysis, the dimorphic property of bones comprises a basis for gender determination with measurements taken on only bones. In this work, 9 different bones such as cranium, mandibula, femur, patella, calcaneus, condylus occipitalis, sternum, hand bones, and foot bones were used for gender determination. Machine learning methods and artificial neural networks, especially linear and quadratic discriminant analysis, while determining the gender, machine learning also were technically adopted. 13 different machine learning algorithms were used as a model for gender determination. Many tools were designed to perform processes like designing necessary bookmarks to try models, designing measurements where machine learning algorithms are used as features, determining coordinates of designed bookmarks, and computation of features. A software named Sekazu was developed by presenting an integrated solution proposal. Thanks to the developed software, models used in gender determination were developed and tried in a fast way and researchers can obtain results reported based on performance metrics flexiblyÖğe Sex estimation using sternum part lenghts by means of artificial neural networks(Elsevier Ireland Ltd, 2019) Oner, Zulal; Turan, Muhammed Kamil; Oner, Serkan; Secgin, Yusuf; Sahin, BunyaminIn addition to the pelvis, cranium and phalanges, the sternum is also used for postmortem sex identification. Bone measurements may be obtained on cadaveric bones. Alternatively, computerized tomography may be used to obtain measurements close to the original ones. Moreover, usage of artificial neural networks (ANNs) in the field of medicine has started to provide new horizons. In this study, we aimed to identify sex by an ANN using lengths of manubrium sterni (MSL), corpus sterni (CSL) and processus xiphoideus (XPL) and sternal angle (SA) from computerized tomography (CT) images brought to an orthogonal plane. This study used the thin-slice thoracic CT images of 422 cases (213 female, 209 male) with an age range of 27-60 years brought to the orthogonal plane. Measurements of MSL, CSL, XPL and SA were analyzed with a multilayer artificial neural network that used stochastic gradient descent (SGD) for optimization and two hidden layers. MSL, CSL and XPL were longer, and SA was wider in men (MSL p = 0.000, CSL p = 0.000, XPL p = 0.000, SA p = 0.02). In the case of the two hidden layers of the network with 20 and 14 neurons in the hidden layers, respectively, learning rate of 0.1 and momentum coefficient of 0.9, the accuracy (Acc) of sex prediction was 0.906. In order to define a more realistic performance of the network, bootstrap was run with the confidence interval of 94%. A sensitivity (Sen) value of 0.91 and a specificity (Spe) value of 0.90 were calculated. The success rates that were achieved in sex identification with measurements on the skeleton using ANN were observed to be higher than those achieved by linear models. Also, sometimes all parts of the bones may not be found or might be deformed. In this case, the number of parameters used for the estimation will be incomplete. The ANN has the strong advantage to be able to estimate despite the missing parameter. (C) 2019 Elsevier B.V. All rights reserved.Öğe Smoking prevalence, associated attitudes and comparison of negative automatic thoughts among high school students in turkey(2020) Acat, Murat; Memıs, Çagdas Öykü; Turan, Muhammed Kamil; Benli, Ali Ramazan; Taşkın, Emre; Yaşar, Zehra; Memıs, Seda DerıcıIntroduction: Research indicates that social stressors, negative affect, anxiety or depression areassociated with an increased prevalence of smoking in adolescents.Objective: The aim of this study was to determine the smoking prevalence and to find out whetherspending more time on the internet or psychological characteristics like negative automatic thoughtsand thoughts of failure at school affect smoking among adolescents.Material and Methods: A self-administered anonymous sociodemographic questionnaire and theautomatic thoughts questionnaire (ATQ) were administered using a sample of students in grades 9through 12 at eight different public senior high schools in Karabuk, Turkey.A descriptive analysis was made, and the Chi-square and Mann-Whitney U tests were used to comparethe groups.Results: From the 463 participating students aged 14-19 years (43.9% female, 56.1% male), 40(8.7%) had tried smoking or were former smokers and 48 (10.4%) were occasionally or daily smokers.Students with male gender (p<0.001), older age (Z=-5.356; p<0.001), those who had used alcoholbefore (p<0.001), scored higher on the ATQ (Z=-2.065; p=0.039), spent more time on the internet(Z=-3.021; p=0.003), and felt like failing at school (Z=-3.730; p<0.001), and those who had a smokingmother (p<0.001), father (p=0.005), sibling (p=0.018), or close friend (p<0.001), had a higherfrequency of smoking.Conclusion: In order to increase our understanding, future research on smoking in adolescentscould target the psychological basis of smoking behavior.Öğe A study on sex estimation by using machine learning algorithms with parameters obtained from computerized tomography images of the cranium(Nature Portfolio, 2022) Toy, Seyma; Secgin, Yusuf; Oner, Zulal; Turan, Muhammed Kamil; Oner, Serkan; Senol, DenizThe aim of this study is to test whether sex prediction can be made by using machine learning algorithms (ML) with parameters taken from computerized tomography (CT) images of cranium and mandible skeleton which are known to be dimorphic. CT images of the cranium skeletons of 150 men and 150 women were included in the study. 25 parameters determined were tested with different ML algorithms. Accuracy (Acc), Specificity (Spe), Sensitivity (Sen), F1 score (F1), Matthews correlation coefficient (Mcc) values were included as performance criteria and Minitab 17 package program was used in descriptive statistical analyses. p <= 0.05 value was considered as statistically significant. In ML algorithms, the highest prediction was found with 0.90 Acc, 0.80 Mcc, 0.90 Spe, 0.90 Sen, 0.90 F1 values as a result of LR algorithms. As a result of confusion matrix, it was found that 27 of 30 males and 27 of 30 females were predicted correctly. Acc ratios of other MLs were found to be between 0.81 and 0.88. It has been concluded that the LR algorithm to be applied to the parameters obtained from CT images of the cranium skeleton will predict sex with high accuracy.Öğe A study on the correlation between spleen volume estimated via cavalieri principle on computed tomography images with basic hemogram and biochemical blood parameters(Medrang, 2022) Sahin, Necati Emre; Oner, Zulal; Oner, Serkan; Turan, Muhammed KamilConsidering its hematological and immunological functions, spleen is a very important organ. A change occurs in its size as the spleen performs these functions. This study aims to examine the possible relationships between spleen volume and the basic hemogram and biochemical parameters in serum. Multidetector computed tomography images and basic hemogram and biochemical parameters of 74 adult individuals, 34 male and 40 female, who were found to be healthy, were used in the study. Spleen volume was estimated using the Cavalieri method on multidetector computed tomography images and the correlations between the volume value with basic hemogram and biochemistry parameters were researched. While negative significant correlations were found between the estimated spleen volume and lymphocyte percentage (r=-0.224) and platelet level (r=-0.271); positive significant correlations were found between hemoglobin level (r=0.228), hematocrit level (r=0.237), alanine aminotransferase (r=0.345), and erythrocyte level (r=0.375). As a result of this study, a relationship was found between spleen volume and lymphocyte percentage, hematocrit level, erythrocyte level, platelet level, and alanine aminotransferase level in serum. We believe that the results of the study will provide a larger perspective to clinicians in the diagnosis of diseases associated with spleen.Öğe A trial on artificial neural networks in predicting sex through bone length measurements on the first and fifth phalanges and metatarsals(Pergamon-Elsevier Science Ltd, 2019) Turan, Muhammed Kamil; Oner, Zulal; Secgin, Yusuf; Oner, SerkanBackground: Predicting sex is an important problem in forensic medicine. The femur, patella, mandible and calcaneus bones are frequently used in predicting sex. In our study, we aimed to use the artificial neural network (ANN) technique to predict sex by measuring the values of the phalanges of the first and fifth toes and the first and fifth metatarsal bones. Method: All bone measurements were conducted on the direct X-ray images of 176 males and 178 females in the age range of 24-60 years. The multilayer perceptron classifier (MLPC) input layer included parameters on the bone length measurements of phalanx proximalis I, phalanx distalis I, metatarsal I, phalanx proximalis V, phalanx medialis V, phalanx distalis V and metatarsal V. The output layer contained two neurons to define the male and female sexes. The present study used an MLPC model that had two hidden layers, and the first and second hidden layers contained 14 and 7 nodes, respectively. Results: The model had an overall accuracy (Acc) of 0.95, specificity (Spe) of 0.97, sensitivity (Sen) of 0.95 and Matthews correlation coefficient (Mcc) of 0.92. While the sex prediction success of our proposed model was higher in women, the results were more specific in men and more sensitive in women (Acc(male) = 0.93, Acc(Female) = 0.98, Sen(male) = 0.93, Spe(male) = 0.98, Sen(Female) = 0.98 and Spe(Female) = 0.93). Conclusions: This study demonstrated that the ANN model for length measurements on small bones is a highly effective instrument for sex prediction.
