Brain tumor classification using modified local binary patterns (LBP) feature extraction methods

Kaplan, Kaplan; Kaya, Yılmaz; Kuncan, Melih; Ertunç, H. Metin

doi:10.1016/j.mehy.2020.109696

Cited by 209 publications

(102 citation statements)

References 28 publications

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“…LBP is used for two-dimensional images. However, the 1D-LBP method has been developed to be used in different application areas in data processing for signals with a time-series dimensional sequence [30]- [33].…”

Section: Theoretical Information and Methodology A One Dimensional Local Binary Patterns (1d-lbp)mentioning

confidence: 99%

An Intelligent Approach for Bearing Fault Diagnosis: Combination of 1D-LBP and GRA

Kuncan

2020

IEEE Access

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Bearings are vital automation machine elements that are used quite frequently for power transmission and shaft bearing in rotating machines. The healthy operation of the bearings directly affects the performance of the rotating machines. Bearing faults may cause more vibration than normal in rotating machines, which wastes power. However, further bearing failures can cause vital damage to rotating machines. In this study, bearing vibration values are obtained through a special test setup. Different types and different sizes of artificial faults have been created in the bearings for the testing process. Data on these bearings are collected at different speeds. The purpose of the study is to diagnose faults in the bearings. In this context, a new approach is proposed. First, the one-dimensional local binary pattern (1D-LBP) method is applied to vibration signals, and all signal data are carried to the 1D-LBP plane. Statistical features are obtained from the signals in the 1D-LBP plane by using these features, and then the vibrational signals are classified by the gray relational analysis (GRA) model. Four different data sets are organized to test the proposed approach. The results of the test process with this proposed model have an accuracy of 99.044% for Dataset1 (different speed − 300 rpm intervals), 94.224% for Dataset2 (different speed −60 rpm intervals), and 99.584% for Dataset3 (fault size (mm)); a 100% average success rate is observed for Dataset4 (fault type -error free bearing (EFB), inner ring fault (IRF), outer ring fault (ORF), and ball fault (BF)).

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Section: Theoretical Information and Methodology A One Dimensional Local Binary Patterns (1d-lbp)mentioning

confidence: 99%

An Intelligent Approach for Bearing Fault Diagnosis: Combination of 1D-LBP and GRA

Kuncan

2020

IEEE Access

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“…Proposed by Kaplan et al [ 14 ], the alpha LBP operator calculates the value of each pixel based on an angle value α. The code is the same of standard LBP, although the neighbors considered are on a line as shown in Figure 3 a.…”

Section: The Proposed Methodsmentioning

confidence: 99%

Deep Learning and Handcrafted Features for Virus Image Classification

et al. 2020

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In this work, we present an ensemble of descriptors for the classification of virus images acquired using transmission electron microscopy. We trained multiple support vector machines on different sets of features extracted from the data. We used both handcrafted algorithms and a pretrained deep neural network as feature extractors. The proposed fusion strongly boosts the performance obtained by each stand-alone approach, obtaining state of the art performance.

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“…The LBP is defined as a statistical model applied in image processing that accomplishes efficient features from images [34]. It is highly used in the computer vision domain.…”

Section: Lbp Featuresmentioning

confidence: 99%

Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images

et al. 2021

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In recent times, COVID-19 infection gets increased exponentially with the existence of a restricted number of rapid testing kits. Several studies have reported the COVID-19 diagnosis model from chest X-ray images. But the diagnosis of COVID-19 patients from chest X-ray images is a tedious process as the bilateral modifications are considered an ill-posed problem. This paper presents a new metaheuristic-based fusion model for COVID-19 diagnosis using chest X-ray images. The proposed model comprises different preprocessing, feature extraction, and classification processes. Initially, the Weiner filtering (WF) technique is used for the preprocessing of images. Then, the fusion-based feature extraction process takes place by the incorporation of gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRM), and local binary patterns (LBP). Afterward, the salp swarm algorithm (SSA) selected the optimal feature subset. Finally, an artificial neural network (ANN) is applied as a classification process to classify infected and healthy patients. The proposed model's performance has been assessed using the Chest X-ray image dataset, and the results are examined under diverse aspects. The obtained results confirmed the presented model's superior performance over the state of art methods.

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Brain tumor classification using modified local binary patterns (LBP) feature extraction methods

Cited by 209 publications

References 28 publications

An Intelligent Approach for Bearing Fault Diagnosis: Combination of 1D-LBP and GRA

An Intelligent Approach for Bearing Fault Diagnosis: Combination of 1D-LBP and GRA

Deep Learning and Handcrafted Features for Virus Image Classification

Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images

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