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Öğe Application of complex discrete wavelet transform in classification of Doppler signals using complex-valued artificial neural network(ELSEVIER, 2008) Ceylan, Murat; Ceylan, Rahime; Oezbay, Yueksel; Kara, SadikObjective: In biomedical signal classification, due to the huge amount of data, to compress the biomedical waveform data is vital. This paper presents two different structures formed using feature extraction algorithms to decrease size of feature set in training and test data. Materials and methods: The proposed structures, named as wavelet transform-complex-valued artificial neural network (WT-CVANN) and complex wavelet transform-complex-valued artificial neural network (CWT-CVANN), use real and complex discrete wavelet transform for feature extraction. The aim of using wavelet transform is to compress data and to reduce training time of network without decreasing accuracy rate. In this study, the presented structures were applied to the problem of classification in carotid arterial Doppler ultrasound signals. Carotid arterial Doppler ultrasound signals were acquired from left carotid arteries of 38 patients and 40 healthy volunteers. The patient group included 22 mates and 16 females with an established diagnosis of the early phase of atherosclerosis through coronary or aortofemoropopliteal (tower extremity) angiographies (mean age, 59 years; range, 48-72 years). Healthy volunteers were young non-smokers who seem to not bear any risk of atherosclerosis, including 28 mates and 12 females (mean age, 23 years; range, 19-27 years). Results and conclusion: Sensitivity, specificity and average detection rate were calculated for comparison, after training and test phases of all structures finished. These parameters have demonstrated that training times of CVANN and real-valued artificial neural network (RVANN) were reduced using feature extraction algorithms without decreasing accuracy rate in accordance to our aim. (C) 2008 Elsevier B.V. All rights reserved.Öğe Artificial Neural Network Based on Rotation Forest for Biomedical Pattern Classification(IEEE, 2013) Koyuncu, Hasan; Ceylan, RahimeThe novel classifier system based on ensemble classifier is proposed in this paper. Rotation forest algorithm based on principal component algorithm was used as ensemble classifier method. In presented classifier system, artificial neural network was used as base classifier in this ensemble classifier system. Rotation forest structure has been generally realized with decision trees in literature. But, multilayer perceptron neural network was utilized as base classifier in rotation forest structure in our study. However, principal component analysis was used for obtaining different feature sets from original data set. The proposed RF-ANN structure was applied to Wisconsin breast cancer data taken form UCI Database. The obtained results were compared with the results of neural network optimized particle swarm optimization (PSO-ANN). The realized experimental studies were represented that RF-ANN structure was successful than PSO-ANN structure. RF-ANN classified breast cancer dataset with 98.05% classification accuracy using 9 classifiers.Öğe Bulanık sinir ağ sisteminin ayarlanabilir parametrelerinin analizi ve uygulamaları(Selçuk Üniversitesi Sosyal Bilimler Enstitüsü, 2004-01-12) Ceylan, Rahime; Özbay, Yükselİnsan beyni sayısal bir işlemi birkaç dakikada yapabilmesine karşın, idrak etmeye yönelik olayları çok kısa bir sürede yapar. Fakat bilgisayarlar çok karmaşık işlemleri anında çözümleyebilmelerine karşın, idrak etme ve deneyimlerle kazanılmış bilgileri kullanabilme noktasında çok yetersizdirler. Günümüzde pek çok uygulama alanı bulan Bulanık Mantık, Yapay Sinir Ağları ve Genetik Algoritma gibi yapay zeka metotları da insan beyninin bu özelliğinin gerçekleştirilmesi çabasından ortaya çıkmıştır. Yapay sinir ağları (YSA), çeşitli bağlantılarla birbirine bağlı birimlerden oluşmuş sistemlerdir. Her birim basitleştirilmiş bir nöronun niteliklerini taşır. Sinirsel ağ içindeki birimler her birinin belli bir işlevi olan katmanlardan oluşmaktadır. Son yıllarda YSA ile birlikte kullanılmaya başlanan bulanık mantık ise bulanık küme teorisine dayanan bir matematiksel disiplindir. Bulanık mantık, insan mantığında olduğu gibi sıcak-soğuk yerine sıcak-ılık-soğuk gibi ara değerlere göre çalışmaktadır. Bu tez çalışmasında, yapay sinir ağları ve bulanık c-ortalamalar kümeleme algoritmasının birleştirilmesi ile yeni bir yapı geliştirilmiştir. Geliştirilen yapı ile YSA' da, bazı uygulamalarda çok uzun süren eğitme aşamasında aynı performansı sağlamak kaydı ile sürenin azaltılması amaçlanmıştır. Geliştirilen Bulanık Kümeleme Yapay Sinir Ağları (BKYSA) yapısı ile 3 farklı uygulama üzerinde çalışılmıştır. Bu uygulamalardan birincisi, XOR probleminin çözümü olarak seçilmiştir. İkinci uygulamada, yapay olarak üretilmiş datalar BKYSA ve YSA yapısı ile ayrı ayrı sınıflandırılmış ve sonuçlar karşılaştırmalı olarak sunulmuştur. Üçüncü uygulamada ise, MIT-BIH ECG Arrhythmia Database' den alman 10 farklı aritmiden oluşan bir eğitim data seti oluşturulmuştur. Bu eğitim seti ile eğitilen YSA yapısında, eğitim hatası % 0.04 olarak elde edilmiştir. Bu eğitim seti %37 oranında azaltılarak elde edilen yeni eğitim seti ile BKYSA yapısı eğitilmiş ve yapının eğitim hatası %9.92e-10 olarak elde edilmiştir. Daha sonra, eğitilen her iki yapıda S.Ü. Tıp Fakültesi Kardiyoloji A.B.D. kliniğinden kayıt edilen 92 hasta datası ile test edilmiştir. Testler sonueunda elde edilen toplam test hatası, YSA için %1.48 iken BKYSA için %0.19 olarak bulunmuştur. Eğitme süresi olarak, BKYSA' da YSA' ya göre azaltılmıştır.Öğe Classification of Adrenal Lesions by Bounded PSO-NN(IEEE, 2017) Koyuncu, Hasan; Ceylan, RahimeAdrenal 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.Öğe Classification of carotid artery Doppler signals in the early phase of atherosclerosis using complex-valued artificial neural network(PERGAMON-ELSEVIER SCIENCE LTD, 2007) Ceylan, Murat; Ceylan, Rahime; Dirgenali, Fatma; Kara, Sadik; Ozbay, YukselIn this study, carotid arterial Doppler ultrasound signals were acquired from left carotid arteries of 38 patients and 40 healthy volunteers. The patient group had an established diagnosis of the early phase of atherosclerosis through coronary or aortofemoropopliteal angiographies. Results were classified using complex-valued artificial neural network (CVANN). Principal component analysis (PCA) and fuzzy c-means clustering (FCM) algorithm were used to make a CVANN system more effective. For this aim, before classifying with CVANN, PCA method was used for feature extraction in PCA-CVANN architecture and FCM algorithm was used for data set reduction in FCM-CVANN architecture. Training and test data were selected randomly using 10-fold cross validation. PCA-CVANN and FCM-CVANN architectures classified healthy and unhealthy subjects for training and test data with about 100% correct classification rate. These results shown that PCA-CVANN and FCM-CVANN classified Doppler signals successfully. (c) 2005 Elsevier Ltd. All rights reserved.Öğe Classification of ECG Arrhythmias using Type-2 Fuzzy Clustering Neural Network(IEEE, 2009) Ceylan, Rahime; Ozbay, Yueksel; Karlik, BekirIn this study, Type-2 Fuzzy Clustering Neural Network (T2FCNN) architecture realized for classification of electrocardiography arrhythmias is presented. Type-2 fuzzy clustering neural network is cascade structure formed by clustering and classification stages. In T2FCNN architecture, clustering stage consisted of select best patterns in all patterns that belongs to same class is executed by type-2 fuzzy c-means clustering (T2FCM). The aim of using T2FCM clustering algorithm is to reduce classification error of neural network by optimization of training pattern set. A new training set consisted of cluster centers obtained by type-2 fuzzy c-means clustering algorithm for each class as separately is formed inputs of neural network. Neural network is trained using backpropagation algorithm. Proposed structure is used classification of five ECG signal class composed normal sinus rhythm, sinus bradycardia, sinus arrhythmia, right bundle branch block and left bundle branch block. Data used in this study is obtained from Physionet database, that belongs to MIT-BIH ECG Arrhythmia Database. In the end of making applications, proposed T2FCNN structure is classified ECG arrhythmias with 99% detection rate.Öğe Classification of Mammogram Images by Dictionary Learning(IEEE, 2017) Barstugan, Mucahid; Ceylan, RahimeDictionary 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%.Öğe Comparison of AIS and fuzzy c-means clustering methods on the classification of breast cancer and diabetes datasets(TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, 2014) Ozsen, Seral; Ceylan, RahimeData reduction is an indispensable part of pattern classification processes in many cases. If the number of samples is excessive, sample reduction or data reduction algorithms can be used for an effective processing time and reliable successive results. Many methods have been used for data reduction. Fuzzy c-means is one of these methods and it is widely used in such applications as clustering algorithms. In this study, we applied a different clustering algorithm, an artificial immune system (AIS), for the data reduction process. We realized the performance evaluation experiments on the standard Chain link and Iris datasets, while the main application was conducted using the Wisconsin Breast Cancer and Pima Indian datasets, which were taken from the University of California, Irvine Machine Learning Repository. For these datasets, the performance of the AIS in the data reduction process was compared with the fuzzy c-means clustering algorithm, in which a multilayer perceptron artificial neural network was used as a classifier after the data reduction processes. The obtained results show that the maximum classification accuracies were obtained as 73.96% for the Pima Indian Diabetes dataset and 97.80% for the Wisconsin Breast Cancer dataset with the AIS and the compression rates were 80% and 40% for these results. For fuzzy c-means clustering, however, the aforementioned accuracies were obtained as 63% and 86.69% for the Pima Indian Diabetes and Wisconsin Breast Cancer datasets, respectively. Moreover, the compression rates for these results for fuzzy c-means were 90% and 70%. When the mean classification accuracy values over the experimented compression rates were taken into consideration, the AIS reached a mean classification accuracy of 70.07% for the Pima Indian Diabetes dataset, while 47.64% was obtained by fuzzy c-means for this dataset. For the Wisconsin Breast Cancer dataset, however, the mean classification accuracies of the AIS and fuzzy c-means methods were recorded as 94.90% and 75.43%, respectively.Öğe Comparison of complex-valued neural network and fuzzy clustering complex-valued neural network for load-flow analysis(SPRINGER-VERLAG BERLIN, 2006) Ceylan, Murat; Cetinkaya, Nurettin; Ceylan, Rahime; Ozbay, YukselNeural networks (NNs) have been widely used in the power industry for applications such as fault classification, protection, fault diagnosis, relaying schemes, load forecasting, power generation and optimal power flow etc. Most of NNs are built upon the environment of real numbers. However, it is well known that in computations related to electric power systems, such as load-flow analysis and fault level estimation etc., complex numbers are extensively involved. The reactive power drawn from a substation, the impedance, busbar voltages and currents are all expressed in complex numbers. Hence, NNs in the complex domain must be adopted for these applications. This paper proposes the complexvalued neural network (CVNN) and a new fuzzy clustering complex-valued neural network (FC-CVNN) to estimate busbar voltages in a load-flow problem. The aim of this paper is to present a comparative study of estimation busbar voltages in load-flow analysis using the conventional neural network (real-valued neural network, RVNN), the CVNN and the new FC-CVNN. The results suggest that a new proposed FC-CVNN and CVNN architecture can generalize better than ordinary RVNN and the FC-CVNN is also learn faster.Öğe Comparison of FCM, PCA and WT techniques for classification ECG arrhythmias using artificial neural network(PERGAMON-ELSEVIER SCIENCE LTD, 2007) Ceylan, Rahime; Ozbay, YukselPrincipal component analysis (PCA) and wavelet transform (WT) are two powerful techniques for feature extraction. In addition, fuzzy c-means clustering (FCM) is among considerable techniques for data reduction. In other words, the aim of using FCM is to decrease the number of segments by grouping similar segments in training data. In this paper, four different structures, FCM-NN, PCA-NN, FCM-PCA-NN and WT-NN, are formed by using these two techniques and fuzzy c-means clustering. In addition, FCM-PCA-NN is the new method proposed in this paper for classification of ECG. This paper presents a comparative study of the classification accuracy of ECG signals by using these four structures for computationally efficient early diagnosis. Neural network used in this study is a well-known neural network architecture named as multi-layered perceptron (MLP) with backpropagation training algorithm. The ECG signals taken from MIT-BIH ECG database, are used in training to classify 10 different arrhythmias. These are normal sinus rhythm, sinus bradycardia, ventricular tachycardia, sinus arrhythmia, atrial premature contraction, paced beat, right bundle branch block, left bundle branch block, atrial fibrillation and atrial flutter. Before testing, the proposed structures are trained by backpropagation algorithm. All of the structures are tested by using experimental ECG records of 92 patients (40 male and 52 female, average age is 39.75 +/- 19.06). The test results suggest that FCM-PCA-NN structure can generalize better than PCA-NN and is faster than NN, FCM-NN and WT-NN. (c) 2006 Elsevier Ltd. All rights reserved.Öğe Compensatory Neurofuzzy Model for Discrete Data Classification in Biomedical(SPIE-INT SOC OPTICAL ENGINEERING, 2015) Ceylan, RahimeBiomedical data is separated to two main sections: signals and discrete data. So, studies in this area are about biomedical signal classification or biomedical discrete data classification. There are artificial intelligence models which are relevant to classification of ECG, EMG or EEG signals. In same way, in literature, many models exist for classification of discrete data taken as value of samples which can be results of blood analysis or biopsy in medical process. Each algorithm could not achieve high accuracy rate on classification of signal and discrete data. In this study, compensatory neurofuzzy network model is presented for classification of discrete data in biomedical pattern recognition area. The compensatory neurofuzzy network has a hybrid and binary classifier. In this system, the parameters of fuzzy systems are updated by backpropagation algorithm. The realized classifier model is conducted to two benchmark datasets (Wisconsin Breast Cancer dataset and Pima Indian Diabetes dataset). Experimental studies show that compensatory neurofuzzy network model achieved 96.11% accuracy rate in classification of breast cancer dataset and 69.08% accuracy rate was obtained in experiments made on diabetes dataset with only 10 iterations.Öğe Complex-valued wavelet artificial neural network for Doppler signals classifying(ELSEVIER SCIENCE BV, 2007) Ozbay, Yuksel; Kara, Sadik; Latifoglu, Fatma; Ceylan, Rahime; Ceylan, MuratObjective: In this paper, the new complex-valued wavelet artificial neural network (CVWANN) was proposed for classifying Doppler signals recorded from patients and healthy volunteers. CVWANN was implemented on four different structures (CVWANN-1, -2, -3 and -4). Materials and methods: In this study, carotid arterial Doppler ultrasound signals were acquired from left carotid arteries of 38 patients and 40 healthy volunteers. The patient group had an established diagnosis of the early phase of atherosclerosis through coronary or aortofemoropopliteal angiographies. In implemented structures in this paper, Haar wavelet and Mexican hat wavelet functions were used as real and imaginary parts of activation function on different sequence in hidden layer nodes. CVWANN-1, -2 -3 and -4 were implemented by using Haar-Haar, Mexican hat-Mexican hat, Haar-Mexican hat, Mexican hat-Haar as real-imaginary parts of activation function in hidden layer nodes, respectively. Results and conclusion: In contrast to CVWANN-2, which reached classification rates of 24.5%, CVWANN-1, -3 and -4 classified 40 healthy and 38 unhealthy subjects for both training and test phases with 100% correct classification rate using leave-one-out cross-validation. These networks have 100% sensitivity, 100% specifity and average detection rate is calculated as 100%. In addition, positive predictive value and negative predictive value were obtained as 100% for these networks. These results shown that CVWANN-1, -3 and -4 succeeded to classify Doppler signals. Moreover, training time and processing complexity were decreased considerable amount by using CVWANN-3. As conclusion, using of Mexican hat wavelet function in real and imaginary parts of hidden Layer activation function (CVWANN-2) is not suitable for classifying healthy and unhealthy subjects with high accuracy rate. The cause of unsuitability (obtaining the poor results in CVWANN-2) is tack of harmony between type of activation function in hidden layer and type of input signals in neural network. (c) 2007 Elsevier B.V. All rights reserved.Öğe A Discriminative Dictionary Learning-AdaBoost-SVM Classification Method on Imbalanced Datasets(IEEE, 2017) Barstugan, Mucahid; Ceylan, RahimeSparse representation is a signal processing method which is mostly used in signal compression, noise reduction, and signal and image restoration fields. In this study, sparse representation was used in a different way from the traditional methods. In the proposed method, a hybrid structure was created by combining dictionary learning and ensemble classifier AdaBoost algorithms. The main idea of this method is to obtain the sparse coefficients from an over-complete dictionary and to use the coefficients in the weight update formula of AdaBoost. Support Vector Machines (SVM) classifier was used as weak classifiers of AdaBoost, and AdaBoost-SVM classifier structure was created. Multiplying the sparse coefficients with weight of weak learners process in weight update formula has given satisfying results on imbalanced datasets during the experiments.Öğe Feature Selection using FFS and PCA in biomedical data classification with AdaBoost-SVM(2018) Ceylan, Rahime; Barstugan, MucahidRecently, there has been an increasing trend to propose computer aided diagnosis systems for biomedical pattern recognition. A computer aided diagnosis method, which aims higher classification accuracy, is developed to classify the biomedical dataset. This process includes two types of machine learning algorithms: feature selection and classification. In this method, firstly, features were extracted from biomedical dataset, then the extracted features were classified by hybrid AdaBoost-Support Vector Machines (SVM) classifier structure. For feature selection, Forward Feature Selection (FFS) and Principal Component Analysis (PCA) algorithms were used, and the performance of the feature selection algorithms was tested by AdaBoost-SVM classifier. Following it, advantages and disadvantages of these algorithms were evaluated. Wisconsin Breast Cancer (WBC), Pima Diabetes (PD), Heart (Statlog) biomedical datasets were taken from UCI database and Electrocardiogram (ECG) signals were taken from Physionet ECG Database, and were used to test the proposed hybrid structure. The used two hybrid structures and other studies in the literature were compared with our findings. The obtained results show that the proposed hybrid structure has high classification accuracy for biomedical data classificationÖğe Full-Automatic Liver Segmentation on Abdominal MR Images(IEEE, 2018) Barstugan, Mucahid; Ceylan, Rahime; Asoglu, Semih; Cebeci, Hakan; Koplay, MustafaLiver segmentation process is a challenging field in computer-aided medical image analysis. This study implemented liver segmentation on Abdominal MR images. The liver was automatically segmented from images by morphological methods with high performance. Liver segmentation process was implemented on 56 MR images and the segmentation results were examined. In this study, an effective and fast method was proposed. Seven evaluation metrics (sensitivity, specificity, accuracy, precision, Dice coefficient, Jaccard rate, Structural Similarity Index (SSIM)) were used to test the performance of the proposed method. Mean Dice coefficient value was obtained as 91.701%, mean Jaccard rate value was obtained as 85.052% on 56 images. Segmentation duration for an image (T1 and T2 phases) was found as 2.828 seconds with the proposed method.Öğe Fuzzy clustering complex-valued neural network to diagnose cirrhosis disease(PERGAMON-ELSEVIER SCIENCE LTD, 2011) Ceylan, Rahime; Ceylan, Murat; Ozbay, Yuksel; Kara, SadikIn this study, fuzzy clustering complex-valued neural network (FCCVNN) was proposed to classify portal vein Doppler signals recorded from 54 patients with cirrhosis and 36 healthy subjects. This proposed neural network is a new model for biomedical pattern classification. The FCCVNN was composed of three phases: fuzzy clustering, calculation of FFT values and complex-valued neural network (CVNN). In first phase, fuzzy clustering was done to reduce the number of segments in training pattern. After that, FFT values of Doppler signals were calculated for pre-processing and then obtained values, which include real and imaginary components, were used as the inputs of the CVNN for classification of Doppler signals. Classification results of FCCVNN were evaluated by the different performance evaluation criterion in literature. It shows that Doppler signals were classified successfully with 100% correct classification rate using the proposed method. Moreover, the rates of sensitivity and specificity were calculated as 100% using FCCVNN method. These results were seen to be appropriate with the expected results that are derived from physician's direct diagnosis. This method would be assisted the physician to make the final decision. (C) 2011 Elsevier Ltd. All rights reserved.Öğe Fuzzy clustering complex-valued neural network to diagnose cirrhosis disease (vol 38, pg 9744, 2011)(PERGAMON-ELSEVIER SCIENCE LTD, 2011) Ceylan, Rahime; Ceylan, Murat; Ozbay, Yuksel; Kara, Sadik[Abstract not Available]Öğe A Fuzzy Clustering Neural Network Architecture for Classification of ECG Arrhythmias(Pergamon-elsevier Science Ltd, 2006) Özbay, Yüksel; Ceylan, Rahime; Karlik, BekirAccurate and computationally efficient means of classifying electrocardiography (ECG) arrhythmias has been the subject of considerable research effort in recent years. This study presents a comparative study of the classification accuracy of ECG signals using a well-known neural network architecture named multi-layered perceptron (MLP) with backpropagation training algorithm, and a new fuzzy clustering NN architecture (FCNN) for early diagnosis. The ECG signals are taken from MIT-BIH ECG database, which are used to classify 10 different arrhythmias for training. These are normal sinus rhythm, sinus bradycardia, ventricular tachycardia, sinus arrhythmia, atrial premature contraction, paced beat, right bundle branch block, left bundle branch block, atrial fibrillation and atrial flutter. For testing, the proposed structures were trained by backpropagation algorithm. Both of them tested using experimental ECG records of 92 patients (40 male and 52 female, average age is 39.75 +/- 19.06). The test results suggest that a new proposed FCNN architecture can generalize better than ordinary MLP architecture and also learn better and faster. The advantage of proposed structure is a result of decreasing the number of segments by grouping similar segments in training data with fuzzy c-means clustering.Öğe A Hybrid Tool on Denoising and Enhancement of Abdominal CT Images before Organ & Tumour segmentation(IEEE, 2017) Koyuncu, Hasan; Ceylan, RahimeMost of abdominal CT images include Gaussian noise, and CT scans form a blurry vision because of the internal fat tissue inside of abdomen. These two handicaps (noise and fat tissue) constitute an impediment in front of an accurate abdominal organ & tumour segmentation. Also segmentation techniques generally fall into error on segmentation of close grayscale regions. Therefore, denoising and enhancement parts are crucial for better segmentation results on CT images. In this paper, we form a tool including three efficient algorithms for the purpose of image enhancement before abdominal organ & tumour segmentation. At first, the denoising process is realized by Block Matching and 3D Filtering (BM3D) algorithm for elimination of Gaussian noise stated in arterial phase CT images. At second, Fast Linking Spiking Cortical Model (FL-SCM) is used for removing the internal fat tissue. At last, Otsu algorithm is processed to remove the redundant parts within the image. In experiments, Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) index are used to evaluate the performance of proposed method, and a visual comparison is presented. According to results, it is seen that proposed tool obtains the best PSNR and SSIM values in comparison with two steps of pipeline (FL-SCM and BM3D & FL-SCM). Consequently, BM3D & FL-SCM & Otsu (BFO) ensures a clean abdomen particularly for segmentation of liver, spleen, pancreas, adrenal tumours, aorta, ribs, spinal cord and kidneys.Öğe Integration of type-2 fuzzy clustering and wavelet transform in a neural network based ECG classifier(PERGAMON-ELSEVIER SCIENCE LTD, 2011) Ozbay, Yuksel; Ceylan, Rahime; Karlik, BekirThis paper presents a new automated diagnostic system to classification of electrocardiogram (ECG) arrhythmias. The diagnostic system is executed using type-2 fuzzy c-means clustering (T2FCM) algorithm, wavelet transform (WT) and neural network. Method of combining T2FCM and WT is used to improve performance of neural network. We aimed high accuracy rate to classification of ECG beats and constituted the automated diagnostic system to improve of classifier's performance. Ten types of ECG beats selected from MIT-BIH database were used to train the system. Then, this system was tested by the ECG signals of patients. The classification accuracy of the proposed classifier, type-2 fuzzy clustering wavelet neural network (T2FCWNN), is compared with the structures formed by type-1 FCM and WT. Process of T2FCWNN architecture is realized on three stages. First stage is formed the new training set obtained by selection of the best segments for each arrhythmia class using T2FCM. Second stage is feature extraction by WT on the new training set. Third stage is classification of the extracted features using neural network. The research showed that accuracy rate was found as 99% using this system. (C) 2010 Elsevier Ltd. All rights reserved.