Lung tumour detection by fusing extended local binary patterns and weighted orientation of difference from computed tomography

Shakoor, Mohammad Hossein

doi:10.1049/iet-ipr.2018.5070

Cited by 5 publications

(5 citation statements)

References 36 publications

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“…Several approaches have been proposed for the detection of lung cancer. For example, Shakoor et al 8 presented an extended local binary pattern (ELBP) for CAD‐based lung tumor diagnosis. The ELBP presented an appropriate result for determining non‐uniform patterns.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Novel computer‐aided lung cancer detection based on convolutional neural network‐based and feature‐based classifiers using metaheuristics

Guo

et al. 2021

Int J Imaging Syst Tech

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This study proposes a lung cancer diagnosis system based on computed tomography (CT) scan images for the detection of the disease. The proposed method uses a sequential approach to achieve this goal. Consequently, two well‐organized classifiers, the convolutional neural network (CNN) and feature‐based methodology, have been used. In the first step, the CNN classifier is optimized using a newly designed optimization method called the improved Harris hawk optimizer. This method is applied to the dataset, and the classification is commenced. If the disease cannot be detected via this method, the results are conveyed to the second classifier, that is, the feature‐based method. This classifier, including Haralick and LBP features, is subsequently applied to the received dataset from the CNN classifier. Finally, if the feature‐based method also does not detect cancer, the case study is healthy; otherwise, the case study is cancerous.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Novel computer‐aided lung cancer detection based on convolutional neural network‐based and feature‐based classifiers using metaheuristics

Guo

et al. 2021

Int J Imaging Syst Tech

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“…The same as other previous papers [22,25,34], the aim of this paper is related to achieve highest classification rate. The classification rate is obtained by dividing the number of textures that classified correctly to all of the textures.…”

Section: A Dissimilarity Metric Methodsmentioning

confidence: 83%

“…Some of the popular methods such as chi-square metric, log-likelihood ratio, and histogram intersection [16] are used for this purpose. Such as some scientific papers [22,25] in this paper, chi-square is employed for the K-NN distance metric. Relation (15) shows the chi-square method.…”

Section: A Dissimilarity Metric Methodsmentioning

confidence: 99%

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Weighted Constraint Feature Selection of Local Descriptor for Texture Image Classification

Gemeay,

Alenizi,

Mohammed

et al. 2023

IEEE Access

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There are several statistical descriptors for feature extraction from texture images. Local binary pattern is one of the most popular descriptor for revealing the underlying structure of a texture. Recently several variants of local binary descriptor have been proposed. The completed local binary pattern is an efficient version that can provide discriminant features and consequently providing a high classification rate. It finely characterizes a texture by fusing three histograms of features. Fusing histograms is applied by jointing the histograms and it increases the feature number significantly; therefore, in this paper a weighted constraint feature selection approach is proposed to select a very small number of features without any degradation in classification accuracy. It significantly enhances the classification rate by using a very low number of informative features. The proposed feature selection approach is a filter-based feature selection. It employed a weighted constraint score for each feature. After ranking the features, a threshold estimation method is proposed to select the most discriminant features. For a better comparison, a wide range of different datasets is used as benchmark to assess the compared methods. Implementations on Outex, UIUC, CUReT, MeasTex, Brodatz, Virus, Coral Reef, and ORL face datasets indicate that the proposed method can provide high classification accuracy without any learning step just by selecting a few features of descriptor.

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“…LBP ( Ojala et al 1996 , 2002 ) is a simple yet widely used feature descriptor in image analysis and computer vision for representing local features in images. Existing studies proved that LBP has a striking performance when extracting local features from the images ( Huang et al 2011 , Shakoor 2019 , Ullah et al 2022 ). Considering the outstanding performance of LBP, we proposed an LBP-based feature extraction method for the peptide sequence.…”

Section: Methodsmentioning

confidence: 99%

DeepAVP-TPPred: identification of antiviral peptides using transformed image-based localized descriptors and binary tree growth algorithm

Ullah,

Akbar,

Raza

et al. 2024

Bioinformatics

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Motivation Despite the extensive manufacturing of antiviral drugs and vaccination, viral infections continue to be a major human ailment. Antiviral peptides (AVPs) have emerged as potential candidates in the pursuit of novel antiviral drugs. These peptides show vigorous antiviral activity against a diverse range of viruses by targeting different phases of the viral life cycle. Therefore, the accurate prediction of AVPs is an essential yet challenging task. Lately, many machine learning-based approaches have developed for this purpose; however, their limited capabilities in terms of feature engineering, accuracy, and generalization make these methods restricted. Results In the present study, we aim to develop an efficient machine learning-based approach for the identification of AVPs, referred to as DeepAVP-TPPred, to address the aforementioned problems. First, we extract two new transformed feature sets using our designed image-based feature extraction algorithms and integrate them with an evolutionary information-based feature. Next, these feature sets were optimized using a novel feature selection approach called binary tree growth (BTG) Algorithm. Finally, the optimal feature space from the training dataset was fed to the Deep neural network (DNN) to build the final classification model. The proposed model DeepAVP-TPPred was tested using stringent 5-fold cross-validation and two independent dataset testing methods, which achieved the maximum performance and showed an enhanced efficiency over existing predictors in terms of both accuracy and generalization capabilities. Availability https://github.com/MateeullahKhan/DeepAVP-TPPred Supplementary information Supplementary data are available at Bioinformatics online.

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Lung tumour detection by fusing extended local binary patterns and weighted orientation of difference from computed tomography

Cited by 5 publications

References 36 publications

RETRACTED: Novel computer‐aided lung cancer detection based on convolutional neural network‐based and feature‐based classifiers using metaheuristics

RETRACTED: Novel computer‐aided lung cancer detection based on convolutional neural network‐based and feature‐based classifiers using metaheuristics

Weighted Constraint Feature Selection of Local Descriptor for Texture Image Classification

DeepAVP-TPPred: identification of antiviral peptides using transformed image-based localized descriptors and binary tree growth algorithm

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