Local Binary Pattern Feature Extraction Research Articles

Comparing Local Binary Pattern and Gray Level Co-occurrence Matrix for Feature Extraction in Diabetic Retinopathy Classification

Diabetic retinopathy, a condition characterized by retinal damage and vision loss, is a prevalent complication of diabetes arising from elevated blood sugar levels. With a growing number of individuals affected, efficient and accurate diagnosis is crucial. This study aims to implement and compare the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) feature extraction techniques, which have demonstrated success in prior research. The comparison will provide a comprehensive under- standing of the image features, extract relevant data, and improve the performance of the image analysis pipeline for diabetic retinopathy classification. The result showed that from three scenarios the best accuracy provided by Support Vector Machine with the accuracy score between 73% until 74%, however, other algorithm have little difference which the result on 73%.

Recent Advancement in Prediction and Analyzation of Brain Tumour using the Artificial Intelligence Method

Brain tumour identification and categorization are critical for diagnosis and treatment. This work uses preprocessing and classification algorithms to discover and categories brain tumours. Gaussian smoothing reduces noise and improves image quality, Genetic Algorithms select and optimize features, Deep Learning-Based Segmentation accurately segments tumours, Local Binary Patterns (LBP) extract texture features, and K-Nearest Neighbors classify tumours. Gaussian smoothing reduces noise and improves brain imaging data. Genetic Algorithms extract the most relevant and discriminative features from preprocessed photos. A Deep Learning-Based Segmentation model accurately segments brain tumour regions using these features. After segmentation, Local Binary Patterns (LBP) extract tumour texture features. The K-Nearest Neighbors (KNN) method classifies tumours using these texture features to capture tumour spatial patterns. Our suggested brain tumour detection and classification method combines several techniques to increase accuracy and reliability. Gaussian smoothing and LBP feature extraction improve feature discrimination. Deep Learning-Based Segmentation and the KNN classifier ensure precise tumour location and robust classification. The proposed method will be tested on brain scans including tumour areas. Classification performance measures include accuracy, sensitivity, specificity, and AUC. This work will improve brain tumour detection and classification methods for more accurate diagnoses and treatment planning. The primary goal of the work is to enhance brain tumour identification and categorization using pre processing, classification algorithms, and advanced techniques, ultimately improving diagnosis and treatment outcomes.

Leukemia Blood Cells Detection using Neural Network Classifier

Image segmentation is an image processing operation performed on the image in order to partition the image into some images based on the information contained in the original image. Image segmentation plays an important role in many medical imaging applications, image segmentation facilitates the anatomy process in a particular body of human body. Classification and clustering are the methods used un data mining for analyzing the data sets and divide them on the basis of some particular classification rules. There are many image segmentation tools that used for medical purpose, so it is necessary to define and/or to improve the image segmentation methods in order to get the best method. In this study, the image of leukemia and red blood cells will be used as samples to determine the best algorithm in image segmentation. The procedure for doing segmentation itself is clustering image, edge detection on image, and image classification. The clustering is to extract important information from an image. The edge detection is to determine the existence of edges of lines in image in order to investigate and localize the desired edge features. Moreover, the classification analyzes the properties of some images and organizes the information into certain categories. In this study, the Neural Network and K-Nearest Neighbor are used for image classification by paired with Local Binary Pattern and Principal Component Analysis. The results revealed that the best method of proven in classifying images is from Local Binary Pattern feature extraction with the average accuracy of 94%.

LBP Feature Extraction for Early Detection of Diabetic Eye Diseases

Abstract: Diabetic Eye Diseases, such as diabetic retinopathy, pose a significant threat to the vision of individuals with diabetes. Early detection of these diseases is crucial for effective treatment and prevention of severe vision loss. In this study, we propose a novel approach for the early detection of Diabetic Eye Diseases using Local Binary Patterns (LBP) feature extraction. The LBP method captures local texture patterns in retinal images, allowing for the identification of characteristic features associated with the diseases. To evaluate the effectiveness of our approach, we utilized a dataset consisting of retinal images from diabetic patients with varying stages of the diseases. The dataset was pre-processed using standard techniques, and LBP features were extracted from the segmented retinal regions. A classification algorithm such as SVM, Random Forest, Adaboost was employed to differentiate between normal and diseased retinal images based on the extracted LBP features. Our experimental results demonstrated the efficacy of the proposed LBP feature extraction method, achieving a high accuracy of 79% using Adaboost in MESSIDOR and 85% using SVM in DIARETDB0 dataset in early detection of Diabetic Eye Diseases

QLBP: Dynamic patterns-based feature extraction functions for automatic detection of mental health and cognitive conditions using EEG signals

BackgroundSevere psychiatric disorders, including depressive disorders, schizophrenia spectrum disorders, and intellectual disability, have devastating impacts on vital life domains such as mental, psychosocial, and cognitive functioning and are correlated with an increased risk of mortality. Accurate symptom monitoring and early diagnosis are essential to optimize treatment and enhance patient outcomes. Electroencephalography (EEG) is a potential diagnostic and monitoring tool for mental health and cognitive disorders, as EEG signals are ideal inputs for machine learning models. In this paper, we propose a novel machine learning model for mental disorder detection based on EEG signals. Aimelectroencephalography (EEG) signals for the detection of three major mental health conditions, namely intellectual disability (ID), schizophrenia (SZ), and bipolar disorder (BD); and (ii) to introduce two novel conditional local binary pattern-based feature extractors for precise classification of these three classes. Material and methodWe collected a novel electroencephalography (EEG) signal dataset from 69 individuals, including a control group and participants diagnosed with bipolar disorder, schizophrenia, and intellectual disability. To extract informative features from the dataset, we developed two novel conditional feature extraction functions that improve upon traditional local binary pattern (LBP) functions by utilizing maximum and minimum distance vectors to generate patterns. We refer to these functions as quantum LBP (QLBP). Additionally, we employed wavelet packet decomposition to construct a multileveled feature extraction model. We evaluated several feature selection techniques, including neighborhood component analysis (NCA), Chi2, maximum relevance minimum redundancy (MRMR), and ReliefF, to select the most informative features. Finally, we employed iterative hard majority voting (IHMV) to obtain the final predicted results. ResultsUsing our multichannel electroencephalography (EEG) signal dataset, we calculated channel-by-channel results and voted results for the classification of intellectual disability (ID), schizophrenia (SZ), and bipolar disorder (BD) classes. Our proposed model, employing the k-nearest neighbors (kNN) classifier with the leave-one subject out cross-validation (LOSO CV) strategy, achieved high accuracy rates of 97.47 %, 94.36 %, and 93.49 % for the ID, SZ, and BD classes, respectively. ConclusionsEmploying the leave-one subject out cross-validation (LOSO CV) technique, our proposed model achieved classification accuracy rates of over 90 % for all cases, thereby providing strong evidence for the effectiveness of the proposed quantum local binary pattern (QLBP) feature extraction method.

LBP Özellik Çıkarma ve İstatistiksel Havuzlama Tabanlı Görüntü Spam Tespit Modeli

Email, which stands for electronic mail, is a form of digital communication between two or more individuals. These technological instruments that facilitate communication can have a positive and negative impact on our lives due to junk e-mails, widely known as spam mail. These spam messages, which are typically delivered for commercial purposes by organizations/individuals for indirect or direct benefits, not only distract people but also consume a significant amount of system resources such as processing power, memory, and network bandwidth. In this study, a method based on LBP (Local Binary Patterns) feature extraction and statistical pooling is proposed to classify spam or raw (non-spam) images. Two datasets are used to test the proposed method. The ISH dataset is widely used in the literature and contains 1738 images. In addition to this dataset, the dataset our collect consists of 1015 images in total. Feature extraction was performed on these images. Obtained features were classified by SVM (Support Vector Machine) algorithm. In the proposed method, 98.56% and 79.01% accuracy were calculated for the ISH dataset and our collected dataset, respectively. The results obtained were compared with the studies in the literature.

ExHiF: Alzheimer's disease detection using exemplar histogram-based features with CT and MR images

The classification of medical images is an important priority for clinical research and helps to improve the diagnosis of various disorders. This work aims to classify the neuroradiological features of patients with Alzheimer's disease (AD) using an automatic hand-modeled method with high accuracy. This work uses two (private and public) datasets. The private dataset consists of 3807 magnetic resonance imaging (MRI) and computer tomography (CT) images belonging to two (normal and AD) classes. The second public (Kaggle AD) dataset contains 6400 MR images. The presented classification model comprises three fundamental phases: feature extraction using an exemplar hybrid feature extractor, neighborhood component analysis-based feature selection, and classification utilizing eight different classifiers. The novelty of this model is feature extraction. Vision transformers inspire this phase, and hence 16 exemplars are generated. Histogram-oriented gradients (HOG), local binary pattern (LBP) and local phase quantization (LPQ) feature extraction functions have been applied to each exemplar/patch and raw brain image. Finally, the created features are merged, and the best features are selected using neighborhood component analysis (NCA). These features are fed to eight classifiers to obtain highest classification performance using our proposed method. The presented image classification model uses exemplar histogram-based features; hence, it is called ExHiF. We have developed the ExHiF model with a ten-fold cross-validation strategy using two (private and public) datasets with shallow classifiers. We have obtained 100% classification accuracy using cubic support vector machine (CSVM) and fine k nearest neighbor (FkNN) classifiers for both datasets. Our developed model is ready to be validated with more datasets and has the potential to be employed in mental hospitals to assist neurologists in confirming their manual screening of AD using MRI/CT images.

A Coal Gangue Identification Method Based on HOG Combined with LBP Features and Improved Support Vector Machine

Identification of coal and gangue is one of the important problems in the coal industry. To improve the accuracy of coal gangue identification in the coal mining process, a coal gangue identification method based on histogram of oriented gradient (HOG) combined with local binary pattern (LBP) features and improved support vector machine (SVM) was proposed. First, according to the actual underground working environment of the mine, a machine vision platform for coal gangue identification was built and the coal gangue image acquisition experiment was carried out. Then, the images of coal and gangue were denoised by median filtering, and the coal and gangue features were extracted by using the HOG combined with LBP feature extraction algorithm, and these features were normalized and principal component analysis (PCA) reduced dimension to remove the correlation and redundancy between the features. Finally, SVM, SVM optimized by genetic algorithm (GA-SVM), SVM optimized by particle swarm optimization (PSO-SVM) algorithm, and SVM optimized by grey wolf optimization (GWO-SVM) algorithm were used as classifiers for identification and classification, respectively. The experimental results show that the GWO-SVM classification model has the highest accuracy, and the average classification accuracies were 96.49% and 94.82% of the training set and test set, respectively, which shows that grey wolf algorithm to optimize support vector machine has a good effect on classification of coal gangue images, which proves the feasibility and accuracy of the proposed method.

Corn Leaf Disease Classification Using Local Binary Patterns (LBP) Feature Extraction

Corn is a plant that is widely grown in developing countries such as Indonesia. To increase maize yields, researchers are always innovating on the current state of technology for classifying maize plant diseases. Three kinds of diseases attack corn leaves, namely Gray leaf Spot, Blight, and Common Rush. The amount of data that we use is 3500 data consisting of 500 Gray Leaf Spots, 1000 Blights, 1000 Common Rushes, and 1000 healthy leaves. This study aims to develop an artificial intelligence model. The artificial intelligence model that we developed uses LBP feature extraction combined with k-NN for the classifier. In addition to using the k-NN method, our tests were carried out using several classification methods such as Naïve Bayes and Adaboost. The result of our test is that the k-NN method has the highest value compared to the Naïve Bayes and Adaboost methods. The results of the performance using k-NN with k=5 resulted in a value of 81.1%, the AUC value of 94.1%, the F1-Score of 80.9%, Precision of 81.8%, and Recall of 81.1%.

A novel genetic-artificial neural network based age estimation system

Age estimation is the ability to predict the age of an individual based on facial clues. This could be put to practical use in underage voting detection, underage driving detection, and overage sportsmen detection. To date, no popular automatic age estimation system has been developed to target black faces. This study developed a novel age estimation system from the combination of a genetic algorithm and a back propagation (BP)-trained artificial neural network (ANN) and using the local binary pattern feature extraction technique (LBGANN) targeted at black faces. The system was trained with a predominantly black face database, and the result was compared against that of a standard ANN system (LBANN). The results showed that the developed system LBGANN outperformed the LBANN in terms of the correct classification rate.

MIXTURE FEATURE EXTRACTION BASED ON LOCAL BINARY PATTERN AND GREY-LEVEL CO-OCCURRENCE MATRIX TECHNIQUES FOR MOUTH EXPRESSION RECOGNITION

Some academics struggle to recognize facial emotions based on pattern recognition. In general, this recognition utilizes all facial features. However, this study was limited to identifying facial emotions in a single facial region. In this study, lips, one of the facial features that can reveal a person's expression, are utilized. Using a combination of local binary pattern feature extraction (LBP) and grey level co-occurrence matrix (GLCM) methods and a multiclass support vector machine classification approach for feature extraction in facial images. The concept begins with image segmentation to create an image of a mouth. Experiments were also conducted for various tests, and the outcomes of these experiments revealed a recognition performance of up to 95%. This result was obtained through experiments in which 10% to 40% of the data were evaluated. These findings are beneficial and can be applied to expression recognition in online learning media to monitor the audience's condition directly.

Classification of Chest X-ray COVID-19 Images Using the Local Binary Pattern Feature Extraction Method

Background and Purpose: COVID-19, which started in December 2019, caused significant loss of life and economic losses. Early diagnosis of the COVID-19 is important to reduce the risk of death. Therefore, studies have increased to detect COVID-19 with machine learning methods automatically. Materials and Methods: In this study, the dataset consists of 15153 X-ray images for 4961 patient cases in three classes: Viral Pneumonia, Normal and COVID-19. Firstly, the dataset was preprocessed. And then, the dataset was given to the Cubic Support Vector Machine (Cubic SVM), Linear Discriminant (LD), Quadratic Discriminant (QD), Ensemble, Kernel Naive Bayes (KNB), K-Nearest Neighbor Weighted (KNN Weighted) classification methods as input data. Then, the Local Binary Model (LBP) texture operator was applied for feature extraction. Results: These values were increased from 94.1% (without LBP) to 98.05% using the LBP method. The Cubic SVM method's highest accuracy was observed in these two applications. Conclusions: This study demonstrates that the performance of the presented methods with LBP feature extraction is improved.

Ensemble of features for efficient classification of high-resolution remote sensing image

ABSTRACT Extracting feature is one of the important methods in classification of high-resolution remote sensing image. A good feature set can result in an efficient classification process. Recent trend moves in extracting the features from the image using neural networks with no human intervention. Our approach uses the deep convolutional neural network for extracting deep features. To still rise the efficiency of the extracted features, the proposed system combines the deep features with other features like Gabor features and novel reformed local binary pattern features. The features are combined and sent for classification. Then, the classification process is done to classify the images. The proposed system introduces two novel ideas, in its feature extraction implementation, namely (1) initialisation of filter values for the CNN and (2) change in local binary pattern feature extraction process. The experimental results are carried out with LISS IV Madurai image, and evaluation is done for the verification of the results. It is found that the system proposed produces good results when compared with other existing methods.

COMPLETED LOCAL BINARY PATTERN WITH RANDOM FOREST ON IMAGE CLASSIFICATION

The work develops effective machine learning models for image classification. The work also discusses the performance of random forest with completed local binary pattern feature extraction. Texture feature extraction methods, such as completed local Binary Pattern, can be used to address classification problems successfully

Development of Efficient Classification Systems for the Diagnosis of Melanoma

Skin cancer is usually classified as melanoma and non-melanoma. Melanoma now represents 75% of humans passing away worldwide and is one of the most brutal types of cancer. Previously, studies were not mainly focused on feature extraction of Melanoma, which caused the classification accuracy. However, in this work, Histograms of orientation gradients and local binary patterns feature extraction procedures are used to extract the important features such as asymmetry, symmetry, boundary irregularity, color, diameter, etc., and are removed from both melanoma and non-melanoma images. This proposed Efficient Classification Systems for the Diagnosis of Melanoma (ECSDM) framework consists of different schemes such as preprocessing, segmentation, feature extraction, and classification. We used Machine Learning (ML) and Deep Learning (DL) classifiers in the classification framework. The ML classifier is Naïve Bayes (NB) and Support Vector Machines (SVM). And also, DL classification framework of the Convolution Neural Network (CNN) is used to classify the melanoma and benign images. The results show that the Neural Network (NNET) classifier’ achieves 97.17% of accuracy when contrasting with ML classifiers.

Improvement on KNN using genetic algorithm and combined feature extraction to identify COVID-19 sufferers based on CT scan image

Coronavirus disease 2019 (COVID-19) has spread throughout the world. The detection of this disease is usually carried out using the reverse transcriptase polymerase chain reaction (RT-PCR) swab test. However, limited resources became an obstacle to carrying out the massive test. To solve this problem, computerized tomography (CT) scan images are used as one of the solutions to detect the sufferer. This technique has been used by researchers but mostly using classifiers that required high resources, such as convolutional neural network (CNN). In this study, we proposed a way to classify the CT scan images by using the more efficient classifier, k-nearest neighbors (KNN), for images that are processed using a combination of these feature extraction methods, Haralick, histogram, and local binary pattern. Genetic algorithm is also used for feature selection. The results showed that the proposed method was able to improve KNN performance, with the best accuracy of 93.30% for the combination of Haralick and local binary pattern feature extraction, and the best area under the curve (AUC) for the combination of Haralick, histogram, and local binary pattern with a value of 0.948. The best accuracy of our models also outperforms CNN by a 4.3% margin.

Automated knee ligament injuries classification method based on exemplar pyramid local binary pattern feature extraction and hybrid iterative feature selection

BackgroundKnee ligament injuries have been widely seen in orthopedics and traumatology clinics worldwide. A correct diagnosis is required for treating knee ligament injuries diseases as with other diseases. Magnetic resonance images (MRI) have been often used for the diagnosis of knee ligament injuries. Problem definitionAutomated disease detection methods must be used in clinics to save more time and help medical doctors with diagnosis. This research aims to present an intelligent assistant system to detect knee ligament injuries automatically. MethodThis research presents a new hand-crafted feature generation, and this feature generation model is the exemplar pyramid local binary pattern (LBP) technique. A hybrid feature selector is applied to the generated features for selecting the most valuable/informative features. This feature selector uses ReliefF and Iterative Neighborhood Component Analysis together. The prime objectives of this feature selector are both to select the optimal number of features and using effectiveness both ReliefF and NCA. Two shallow classifiers are used to denote strength both feature generator and used hybrid feature selector. The presented model is tested on three MRI datasets about knee ligament injuries. ResultsThe proposed exemplar pyramid LBP and RFINCA based automated classification method reached 99.32%, 99.56%, and 100.0% classification accuracies for the collected three datasets respectively using the KNN classifier. ConclusionsThese results demonstrated the general and high success of this method. The obtained results were also shown that an intelligent health assistant for knee injuries could be developed by using the proposed exemplar pyramid LBP method.

Local binary pattern and deep learning feature extraction fusion for COVID‐19 detection on computed tomography images

The deadly coronavirus virus (COVID‐19) was confirmed as a pandemic by the World Health Organization (WHO) in December 2019. It is important to identify suspected patients as early as possible in order to control the spread of the virus, improve the efficacy of medical treatment, and, as a result, lower the mortality rate. The adopted method of detecting COVID‐19 is the reverse‐transcription polymerase chain reaction (RT‐PCR), the process is affected by a scarcity of RT‐PCR kits as well as its complexities. Medical imaging using machine learning and deep learning has proved to be one of the most efficient methods of detecting respiratory diseases, but to train machine learning features needs to be extracted manually, and in deep learning, efficiency is affected by deep learning architecture and low data. In this study, handcrafted local binary pattern (LBP) and automatic seven deep learning models extracted features were used to train support vector machines (SVM) and K‐nearest neighbour (KNN) classifiers, to improve the performance of the classifier, a concatenated LBP and deep learning feature was proposed to train the KNN and SVM, based on the performance criteria, the models VGG‐19 + LBP achieved the highest accuracy of 99.4%. The SVM and KNN classifiers trained on the hybrid feature outperform the state of the art model. This shows that the proposed feature can improve the performance of the classifiers in detecting COVID‐19.

Pengenalan Logo Kendaraan Menggunakan Metode Local Binary Pattern dan Random Forest

The vehicle logo is one of the features that can be used to identify a vehicle. Even so, a lot of Intelligent Transport System which are developed nowadays has yet to use a vehicle logo recognition system as one of its vehicle identification tools. Hence there are still cases of traffic crimes that haven't been able to be examined by the system, such as cases of counterfeiting vehicle license plates. Vehicle logo recognition itself could be done by using various feature extraction and classification methods. This research project uses the Local Binary Pattern feature extraction method which is often used for many kinds of image recognition systems. Then, the classification method used is Random Forest which is known to be effective and accurate for various classification problems. The data used for this study were as many as 2000 vehicle logo images consisting of 5 brand classes, namely Honda, Kia, Mazda, Mitsubishi, and Toyota. The results of the tests carried out obtained the best accuracy value of 88.89% for the front view logo image dataset, 77.03% for the side view logo image dataset, and 83% for the dataset with both types of images.

Analysis of Random Local Descriptors in Face Recognition

This article describes and analyzes the new feature extraction technique, Random Local Descriptor (RLD), that is used for the Permutation Coding Neural Classifier (PCNC), and compares it with Local Binary Pattern (LBP-based) feature extraction. The paper presents a model of face feature detection using local descriptors, and describes an improvement on the PCNC for the recognition of plane rotated and small displaced face images, as applied to three databases, i.e., ORL, FRAV3D and FEI. All databases are described along with the recognition results that were obtained. We also include a comparison of our classifier with the Support Vector Machine (SVM) and Iterative Closest Point (ICP). The ORL database was selected to compare our RLDs with LBP-based algorithms. The PCNC with the RLDs demonstrated the best recognition rate, i.e., 97.49%, in comparison with 90.49% for LBPs. For the FEI image database, we obtained the best recognition rate, i.e., 93.57%, in comparison with 66.74% for LBPs. Using the RLDs and rotating the original images for FRAV3D, we improved the recognition rate by decreasing by approximately twice the number of errors. In addition, we analyzed the influence of different RLD parameters on the quality of facial recognition.