The Experts below are selected from a list of 103233 Experts worldwide ranked by ideXlab platform

Berat Dogan - One of the best experts on this subject based on the ideXlab platform.

  • ecg beat Classification using particle swarm optimization and radial basis function neural network
    Expert Systems With Applications, 2010
    Co-Authors: Mehmet Korurek, Berat Dogan
    Abstract:

    This paper presents a method for electrocardiogram (ECG) beat Classification based on particle swarm optimization (PSO) and radial basis function neural network (RBFNN). Six types of beats including Normal Beat, Premature Ventricular Contraction (PVC), Fusion of Ventricular and Normal Beat (F), Atrial Premature Beat (A), Right Bundle Branch Block Beat (R) and Fusion of Paced and Normal Beat (f) are obtained from the MIT-BIH arrhythmia database. Four morphological features are extracted from each beat after the preprocessing of the selected records. For Classification Stage of the extracted features, a RBFNN structure which is evolved by particle swarm optimization is used. Several experiments are performed over the test set and it is observed that the proposed method classifies ECG beats with a smaller size of network without making any concessions on the Classification performance.

  • ecg beat Classification using particle swarm optimization and radial basis function neural network
    Expert Systems With Applications, 2010
    Co-Authors: Mehmet Korurek, Berat Dogan
    Abstract:

    This paper presents a method for electrocardiogram (ECG) beat Classification based on particle swarm optimization (PSO) and radial basis function neural network (RBFNN). Six types of beats including Normal Beat, Premature Ventricular Contraction (PVC), Fusion of Ventricular and Normal Beat (F), Atrial Premature Beat (A), Right Bundle Branch Block Beat (R) and Fusion of Paced and Normal Beat (f) are obtained from the MIT-BIH arrhythmia database. Four morphological features are extracted from each beat after the preprocessing of the selected records. For Classification Stage of the extracted features, a RBFNN structure which is evolved by particle swarm optimization is used. Several experiments are performed over the test set and it is observed that the proposed method classifies ECG beats with a smaller size of network without making any concessions on the Classification performance.

Esin Dogantekin - One of the best experts on this subject based on the ideXlab platform.

  • brain tumor detection based on convolutional neural network with neutrosophic expert maximum fuzzy sure entropy
    Measurement, 2019
    Co-Authors: Fatih Ozyurt, Eser Sert, Engin Avci, Esin Dogantekin
    Abstract:

    Abstract Brain tumor Classification is a challenging task in the field of medical image processing. The present study proposes a hybrid method using Neutrosophy and Convolutional Neural Network (NS-CNN). It aims to classify tumor region areas that are segmented from brain images as benign and malignant. In the first Stage, MRI images were segmented using the neutrosophic set – expert maximum fuzzy-sure entropy (NS-EMFSE) approach. The features of the segmented brain images in the Classification Stage were obtained by CNN and classified using SVM and KNN classifiers. Experimental evaluation was carried out based on 5-fold cross-validation on 80 of benign tumors and 80 of malign tumors. The findings demonstrated that the CNN features displayed a high Classification performance with different classifiers. Experimental results indicate that CNN features displayed a better Classification performance with SVM as simulation results validated output data with an average success of 95.62%.

  • an automatic diabetes diagnosis system based on lda wavelet support vector machine classifier
    Expert Systems With Applications, 2011
    Co-Authors: Duygu Calisir, Esin Dogantekin
    Abstract:

    In this paper, an automatic diagnosis system for diabetes on Linear Discriminant Analysis (LDA) and Morlet Wavelet Support Vector Machine Classifier: LDA-MWSVM is introduced. The structure of this automatic system based on LDA-MWSVM for the diagnosis of diabetes is composed of three Stages: The feature extraction and feature reduction Stage by using the Linear Discriminant Analysis (LDA) method and the Classification Stage by using Morlet Wavelet Support Vector Machine (MWSVM) classifier Stage. The Linear Discriminant Analysis (LDA) is used to separate features variables between healthy and patient (diabetes) data in the first Stage. The healthy and patient (diabetes) features obtained in the first Stage are given to inputs of the MWSVM classifier in the second Stage. Finally, in the third Stage, the correct diagnosis performance of this automatic system based on LDA-MWSVM for the diagnosis of diabetes is calculated by using sensitivity and specificity analysis, Classification accuracy, and confusion matrix, respectively. The Classification accuracy of this system was obtained at about 89.74%.

  • automatic hepatitis diagnosis system based on linear discriminant analysis and adaptive network based on fuzzy inference system
    Expert Systems With Applications, 2009
    Co-Authors: Esin Dogantekin, Akif Dogantekin, Derya Avci
    Abstract:

    In this paper, an automatic diagnosis system based on Linear Discriminant Analysis (LDA) and Adaptive Network based on Fuzzy Inference System (ANFIS) for hepatitis diseases is introduced. This automatic diagnosis system deals with the combination of feature extraction and Classification. This automatic hepatitis diagnosis system has two Stages, which feature extraction - reduction and Classification Stages. In the feature extraction - reduction Stage, the hepatitis features were obtained from UCI Repository of Machine Learning Databases. Then, the number of these features was reduced to 8 from 19 by using Linear Discriminant Analysis (LDA). In the Classification Stage, these reduced features are given to inputs ANFIS classifier. The correct diagnosis performance of the LDA-ANFIS automatic diagnosis system for hepatitis disease is estimated by using Classification accuracy, sensitivity and specificity analysis, respectively. The Classification accuracy of this LDA-ANFIS automatic diagnosis system for the diagnosis of hepatitis disease was obtained in about 94.16%.

Xiaotong Yuan - One of the best experts on this subject based on the ideXlab platform.

  • hyperspectral image Classification using spectral spatial lstms
    Neurocomputing, 2019
    Co-Authors: Feng Zhou, Renlong Hang, Qingshan Liu, Xiaotong Yuan
    Abstract:

    Abstract In this paper, we propose a hyperspectral image (HSI) Classification method using spectral-spatial long short term memory (LSTM) networks. Specifically, for each pixel, we feed its spectral values in different channels into Spectral LSTM one by one to learn the spectral feature. Meanwhile, we firstly use principle component analysis (PCA) to extract the first principle component from a HSI, and then select local image patches centered at each pixel from it. After that, we feed the row vectors of each image patch into Spatial LSTM one by one to learn the spatial feature for the center pixel. In the Classification Stage, the spectral and spatial features of each pixel are fed into softmax classifiers respectively to derive two different results, and a decision fusion strategy is further used to obtain a joint spectral-spatial results. Experimental results on three widely used HSIs (i.e., Indian Pines, Pavia University, and Kennedy Space Center) show that our method can improve the Classification accuracy by at least 2.69%, 1.53% and 1.08% compared to other state-of-the-art methods.

  • hyperspectral image Classification using spectral spatial lstms
    CCF Chinese Conference on Computer Vision, 2017
    Co-Authors: Feng Zhou, Renlong Hang, Qingshan Liu, Xiaotong Yuan
    Abstract:

    In this paper, we propose a hyperspectral image (HSI) Classification method using spectral-spatial long short term memory (LSTM) networks. Specifically, for each pixel, we feed its spectral values in different channels into Spectral LSTM one by one to learn the spectral feature. Meanwhile, we firstly use principle component analysis (PCA) to extract the first principle component from a HSI, and then select local image patches centered at each pixel from it. After that, we feed the row vectors of each image patch into Spatial LSTM one by one to learn the spatial feature for the center pixel. In the Classification Stage, the spectral and spatial features of each pixel are fed into softmax classifiers respectively to derive two different results, and a decision fusion strategy is further used to obtain a joint spectral-spatial results. Experiments are conducted on two widely used HSIs, and the results show that our method can achieve higher performance than other state-of-the-art methods.

Mehmet Korurek - One of the best experts on this subject based on the ideXlab platform.

  • ecg beat Classification using particle swarm optimization and radial basis function neural network
    Expert Systems With Applications, 2010
    Co-Authors: Mehmet Korurek, Berat Dogan
    Abstract:

    This paper presents a method for electrocardiogram (ECG) beat Classification based on particle swarm optimization (PSO) and radial basis function neural network (RBFNN). Six types of beats including Normal Beat, Premature Ventricular Contraction (PVC), Fusion of Ventricular and Normal Beat (F), Atrial Premature Beat (A), Right Bundle Branch Block Beat (R) and Fusion of Paced and Normal Beat (f) are obtained from the MIT-BIH arrhythmia database. Four morphological features are extracted from each beat after the preprocessing of the selected records. For Classification Stage of the extracted features, a RBFNN structure which is evolved by particle swarm optimization is used. Several experiments are performed over the test set and it is observed that the proposed method classifies ECG beats with a smaller size of network without making any concessions on the Classification performance.

  • ecg beat Classification using particle swarm optimization and radial basis function neural network
    Expert Systems With Applications, 2010
    Co-Authors: Mehmet Korurek, Berat Dogan
    Abstract:

    This paper presents a method for electrocardiogram (ECG) beat Classification based on particle swarm optimization (PSO) and radial basis function neural network (RBFNN). Six types of beats including Normal Beat, Premature Ventricular Contraction (PVC), Fusion of Ventricular and Normal Beat (F), Atrial Premature Beat (A), Right Bundle Branch Block Beat (R) and Fusion of Paced and Normal Beat (f) are obtained from the MIT-BIH arrhythmia database. Four morphological features are extracted from each beat after the preprocessing of the selected records. For Classification Stage of the extracted features, a RBFNN structure which is evolved by particle swarm optimization is used. Several experiments are performed over the test set and it is observed that the proposed method classifies ECG beats with a smaller size of network without making any concessions on the Classification performance.

S. Avidan - One of the best experts on this subject based on the ideXlab platform.

  • Support vector tracking
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004
    Co-Authors: S. Avidan
    Abstract:

    Support Vector Tracking (SVT) integrates the Support Vector Machine (SVM) classifier into an optic-flow-based tracker. Instead of minimizing an intensity difference function between successive frames, SVT maximizes the SVM Classification score. To account for large motions between successive frames, we build pyramids from the support vectors and use a coarse-to-fine approach in the Classification Stage. We show results of using SVT for vehicle tracking in image sequences.

  • support vector tracking
    Computer Vision and Pattern Recognition, 2001
    Co-Authors: S. Avidan
    Abstract:

    Support Vector Tracking (SVT) integrates the Support Vector Machine (SVM) classifier into an optic-flow based tracker. Instead of minimizing an intensity difference function between successive frames, SVT maximizes the SVM Classification score. To account for large motions between successive frames, we build pyramids from the support vectors and use a coarse-to-fine approach in the Classification Stage. We show results of using a homogeneous quadratic polynomial kernel-SVT for vehicle tracking in image sequences.