Yazar "Ceylan R." seçeneğine göre listele
Listeleniyor 1 - 8 / 8
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe Adrenal Tumor Classification on T1 and T2-weighted Abdominal MR Images(Institute of Electrical and Electronics Engineers Inc., 2019) Barstugan M.; Ceylan R.; Asoglu S.; Cebeci H.; Koplay M.Adrenal tumors occur on adrenal glands and can be malignant. Adrenal glands consist of cortex and medulla. If cortex or medulla produce hormones extremely, the hormonal unbalance situation arises. This situation causes adrenal tumor occurrence on adrenal glands. In this study, adrenal tumors on T1 and T2-weighted MR images were classified by the SVM algorithm. Before the classification stage, different feature extraction algorithms and filtering methods were used for preprocessing. The classification results that were obtained by four different methods were evaluated on five different evaluation metrics as sensitivity, specificity, accuracy, precision, and F-score. The best classification performance was obtained with Method 2 on T1-weighted MR (Magnetic Resonance) images where the sensitivity, specificity, accuracy, precision, and F-score metrics were obtained as 99.17%, 90%, 98.4%, 99.17%, and 99.13%, respectively. © 2019 IEEE.Öğe Artificial immune systems and ANN in PMR coding [PMR Kodlamasinda YAPAY Ba?işiklik Si?stemleri? VE YSA](2004) Şahan S.; Ceylan R.; Güneş S.This study aims at introducing a new Artificial Intelligence technique especially beginned to be known newly in our country and models human immune system. Artificial Immune Systems (AIS) are increasing their application areas day by day and coming into existence as a prosperius problem solving technique with their performance on these applications. ABNET is an hybrid system proposed by F. N. De Castro, F. J. Von Zuben and Getúlio A. De Deus Jr. that is a combination of Artificial Neural Networks and immune based metaphors. In this study, the coding in PMR (Private Mobile Radio) is performed with ABNET. To evaluate the results, same coding problem is solved with an ANN system, too. With respect to the results of both system, the applicability of ABNET to real world problems is discussed. © 2004 IEEE.Öğe Automatic liver segmentation in abdomen CT images using SLIC and adaboost algorithms(Association for Computing Machinery, 2018) Barstugan M.; Ceylan R.; Sivri M.; Erdogan H.This study is an implementation of liver segmentation on abdomen CT images. The liver organ was segmented by using SLIC super-pixel and AdaBoost algorithms. Firstly, the images were clustered by SLIC super-pixel algorithm. Then, the liver was segmented by AdaBoost classifier. The segmentation process was done automatically. The automatic segmentation is based on the classification of overlapping patches of the image. The results of automatic segmentation and manual segmentation were compared and the efficiency of the method was observed. The best Dice rate was obtained as 92.13% and the best Jaccard rate was obtained as 85.8% on 16 abdomen CT images. © 2018 Association for Computing Machinery.Öğe Classification of adrenal lesions by bounded PSO-NN [Sinirli PSO-YSA Yapisi ile Sürrenal Lezyonlarin Siniflandirilmasi](Institute of Electrical and Electronics Engineers Inc., 2017) Koyuncu H.; Ceylan R.Adrenal glands are the organs at which vitally important hormones are released. In adrenal glands, different kind of benign and malign lesions can arise. Herein, Dynamic Computed Tomography (dynamic CT) is the most used scan type for definition of lesion types. On the events that dynamic CT underwhelms, biopsy process is performed which is difficultly implemented because of the location of adrenal glands. During biopsy process, different complications can happen since adrenals glands are surrounded by spleen, lung, etc. At this point, a decision support system is needed for helping to medical experts. In this study, a Region of Interest (ROI) is defined that includes adrenal lesions. After that, feature extraction is realized by using Gray-Level Co-Occurance Matrix (GLCM) and the second-order statistics. At classification part, Neural Network (NN) and a novel approach including NN (Bounded PSO-NN) are evaluated by utilizing from three performance metrics. As a result, it's confirmed that Bounded PSO-NN classifies the malign and benign patterns more accurately which obtained by analysis taken from ROI. © 2017 IEEE.Öğe Classification of mammogram images by dictionary learning [Sözlük ö?renme ile mamogram görüntülerinin siniflandirilmasi](Institute of Electrical and Electronics Engineers Inc., 2017) Barstu?an M.; Ceylan R.Dictionary Learning is a method used in signal and image processing. In this study, classification of mammogram images were realized by using dictionary learning and sparse representation algorithms. The attributes of the images were detected with Wavelet Transform and PCA, and the new dataset which was created by the obtained attributes were classified by Dictionary Learning. Moreover, the classification performance of the Dictionary Learning algorithm was evaluated by classifying the new dataset with SVM, Rotation Forest and AdaBoost algorithms. The best classification accuracy was obtained by PCA-Dictionary Learning algorithm as 98.89%. © 2017 IEEE.Öğe Comparison of type-2 fuzzy clustering-based cascade classifier models for ECG arrhythmias(World Scientific Publishing Co. Pte Ltd, 2014) Ceylan R.; Özbay Y.; Karlik B.The aim of this study is to present a comparison of the novel cascade classifier models based on fuzzy clustering and feature extraction techniques according to efficiency. These models are composed of three subsystems: The first subsystem is constituted by fuzzy clustering technique to choose the best patterns that ideally show its class attributes in dataset. The second subsystem consists of discrete wavelet transform (DWT) which realizes feature extraction procedure on selected patterns by using fuzzy c-means clustering. The last subsystem implements the classification of extracted features for each pattern using classification algorithm. In this paper, type-2 fuzzy c-means (T2FCM) clustering is used in the first subsystem of the proposed classification models and the new training set is obtained. In the second subsystem, the features of the obtained new training set are extracted with DWT; hence, three different feature sets along with different number of features are formed using Daubechies-2 wavelet function. In the last subsystem of the model, the feature sets are classified using classification algorithm. Here, two different classification algorithms, neural network (NN) and support vector machine (SVM), are used for comparison. Thus, two classification models are implemented and named as T2FCWNN (classifier is NN) and T2FCWSVM (classifier is SVM), respectively. This proposed classifier models have been applied to classify electrocardiogram (ECG) signals. One of the goals of this study is to present a fast and efficient classifier. For this reason, high accuracy rate is been aimed for classification of RR intervals in ECG signal. So, we have utilized T2FCM and WTs to improve the performance of the classification algorithms. Both training and testing set for classifier models have included 12 ECG signal classes. Well-known back propagation algorithm has been used for training of neural networks (NNs). The best testing results have been obtained with 99% recognition accuracy with T2FCWNN-2. © 2014 National Taiwan University.Öğe RF ensemble novelties based on optimized & backpropagated NNs(International Association of Computer Science and Information Technology, 2017) Koyuncu H.; Ceylan R.This paper presents a classifier model based on Rotation Forest (RF) ensemble structure for biomedical data classification. Classifiers based on RF are generally implemented by using Decision Trees. In this study, optimized Neural Network (NN) is preferred as being the base classifier in RF so as to achieve higher classification performance. Two optimization techniques, Artificial Bee Colony Optimization (ABC) and Particle Swarm Optimization (PSO), are utilized to improve the performance of NN for escaping from local minima. In this way, PSO-NN and ABC-NN based RF structures are designed, and they are called as RF (PSO-NN) and RF (ABC-NN), respectively. In these classifiers, initial weights of NNs are found by using PSO or ABC algorithms. The implemented classifiers based on RF are applied to biomedical datasets (Wisconsin Breast Cancer and Pima Indian Diabetes) that are taken from UCI Machine Learning Repository. Furthermore, fourteen different ensemble structures are generated using these algorithms to prove the superiority of the proposed method. When the results are examined using several performance metrics, it is seen that RF (ABC-NN) classifier achieves to more reliable and better results than other classifiers.Öğe ScPSO-based multithresholding modalities for suspicious region detection on mammograms(Elsevier Inc., 2018) Ceylan R.; Koyuncu H.[Abstract not Available]
