GLCM Feature Extraction Research Articles

Performance Analysis of Walsh-Hadamard Transform-Based Gabor Filter Feature Extraction Method and GLCM Feature Extraction Method for Brain Tumor Detection

Performance Analysis of Walsh-Hadamard Transform-Based Gabor Filter Feature Extraction Method and GLCM Feature Extraction Method for Brain Tumor Detection

Effect of Using GLCM and LBP+HOG Feature Extraction on SVM Method in Classification of Human Skin Disease Type

Skin diseases have been ranked third out of ten diseases suffered by outpatients in many hospitals in Indonesia. The public often underestimates these diseases because they are considered not to cause death. In general, dermatologists diagnose skin diseases using the biopsy process, but the biopsy process is quite expensive and can cause injury to the skin. Each skin disease has different texture and shape characteristics, so classification can be used to distinguish the type of skin disease. This study compares LBP+HOG and GLCM feature extraction with the SVM classification method to determine the best feature extraction in skin disease classification. This research compares GLCM and LBP+HOG feature extraction using the SVM method. GLCM is used to measure the relationship between pixel intensities in the image, while LBP+HOG combines information about the texture and shape of the image. The test results show that the GLCM feature extraction method with SVM classification achieves an accuracy of 74%. In this test, the parameters C=100 and the features used are homogeneity, contrast, energy, correlation, ASM, and dissimilarity. Meanwhile, extraction with LBP+HOG resulted in an accuracy of 68%.

Analisis Kinerja Logistic Regression Classifier Berdasarkan Seleksi Fitur Warna, GLCM (Gray Level Co-occurrence Matrix) dan Bentuk (Studi Kasus Jenis Ketupat Khas Bali)

Ketupat is a unique culture and tradition in Bali. Ketupat is often used as a banten offering. This unique procession of the ketupat war is held once a year to coincide with the Purnama Kapat. The ketupat war is a traditional event with participants throwing ketupat at each other. It aims to be grateful for all the gifts that the Creator has given to humans in this world. This tradition has existed since the 1970s with the beginning of its appearance involving two shirtless men. The ketupat war ceremony is part of the yadnya as the basis for the return of the Tri Rna. In today's era of the development of science and technology, people are only focused on meeting material needs, understanding the meaning of rituals and religious events is decreasing, and it is considered an incriminating or even meaningless activity. In this study, a dataset of 90 was used with 18 types of ketupat used, namely: bagia, batu dam-pulan, bracelet, kale, mattress, kedis, kepel, kroso, lepet, pengambean, sai, cow, sari, sirikan, suna, tulud, tumpeng. This study aims to determine the per-formance of Logistic Regression Classifier by using color, HSV, GLCM and shape features. The dataset used is a typical Balinese ketupat type of 90 data. In this test, 4 test scenarios are used: 1) The first test scenario, the dataset input performs the preprocessing process, HSV imagery, and the color feature extraction process where the output results are in the form of hue, saturation and value values in the form of excel files. 2) The second test scenario, the input dataset performs a preprocessing process, grayscale, and performs the GLCM feature extraction process where the output results are in the form of feature values of angle 00, angle 450, angle 900 and angle 1350 in the form of an excel file. 3) The third test scenario, the dataset input performs the preprocessing process, binner, performs the form feature extraction process where the output results are in the form of metric, eccentricity in the form of excel files. 4) The fourth test scenario, from the 3 (three) feature extractions carried out, the new dataset, the next stage is to implement the Logistic Re-gression classifier method to obtain accuracy values. Based on the results of the classification, testing and analysis, the level of accuracy from each of them was obtained with a training accuracy value of 69.84% and a testing accuracy of 22.2%, which means that the classification method was declared ineffective in analyzing the features used in the ketupat classification, so it is necessary to compare it with other methods so that it gets an accuracy result above 90%.

Detection of normal chicken meat and tiren chicken using naïve bayes classifier and glcm feature extraction

Detection of normal chicken meat and tiren chicken using naïve bayes classifier and glcm feature extraction

Recognition of Medicinal Plant Leaf Patterns Using Morphology-Based and GLCM Feature Extraction

Recognition of Medicinal Plant Leaf Patterns Using Morphology-Based and GLCM Feature Extraction

Klasifikasi Rimpang Menggunakan Metode K-Nearest Neighbor dan Ekstraksi Ciri Gray Level Co-occurrence Matrix

Rhizome is a modification of plant stems that grow under the soil surface and function as a storage place for food reserves. This plants have internodes that function produce new shoots and roots. Rhizomes are commonly used by people as spices in cooking and herbal medicine. Rhizomes have many types, such as ginger, sand ginger, fingerroot, turmeric, galangal, and curcuma. These types have similarities to each other, such as texture, shape, and color. These similarities can cause problems such as difficulty in identifying the type of rhizome. The solution to this problem is a computer system that can classify the type of rhizomes. The system in this research was built using the K-Nearest Neighbor method and Gray Level Co-occurrence Matrix texture feature extraction. Research data amounted to 500 images with ginger, sand ginger, fingerroot, turmeric, and galangal classes. The stages of this research are data collection, image resizing, conversion to grayscale, GLCM feature extraction, storing the extraction results into dataframe, dividing data into train data and test data, classification with K-NN, and implement GUI to make operation easier. Accuracy results on this system get a value of 74% on test data and 64% on train data with value of K=11.

Klasifikasi Penyakit Daun Jagung Menggunakan Metode CNN Dengan Image Processing HE Dan CLAHE

This research focuses on classifying diseases on corn leaves using Convolutional Neural Network (CNN) with HE and CLAHE image processing methods. Corn plants, as the main food crop, are susceptible to various diseases that can reduce the quantity and quality of the harvest. With advances in AI technology, CNN is implemented to automatically identify disease symptoms on corn leaves. Previously, research using KNN and GLCM feature extraction resulted in low accuracy. Therefore, this research utilizes HE and CLAHE image processing techniques to improve image quality before classification is carried out. The research results show that CNN with CLAHE achieves the highest accuracy of 95%, while the use of HE produces an accuracy of 91%. Testing successfully identified 149 disease images with CLAHE, while HE classified 145 disease images. The conclusion of the research is that using CNN with CLAHE is more effective in classifying corn leaf diseases compared to HE with an accuracy of 95%. It is hoped that the application of this method can help farmers efficiently identify and overcome diseases in corn plants, supporting increased yields.

A Proposed Batik Automatic Classification System Based on Ensemble Deep Learning and GLCM Feature Extraction Method

A Proposed Batik Automatic Classification System Based on Ensemble Deep Learning and GLCM Feature Extraction Method

REAL TIME DETECTION OF CHICKEN EGG QUANTITY

A common problem currently being faced in the chicken egg production home industry is difficulty in counting the number of eggs. Currently, calculating the number of eggs is still done manually, which is less than optimal and prone to errors, so many entrepreneurs often experience losses. The manual system currently used also has the potential for this to happen. The use of technology on an MSME scale among laying hen breeders has not been widely adopted, this is due to limited access and understanding of technology. One alternative solution to deal with this problem is to build a real-time computerized system. The system that will currently be built in this research uses GLCM feature extraction and the SVM classification method. This system will detect egg production via CCTV cameras and will be stored in a database to be displayed on the website. The advantage of this system is that egg entrepreneurs can monitor chicken egg yields in real time. The results of trials that have been carried out using GLCM feature extraction and the SVM classification method in calculating the number of eggs using the SVM method with a polynomial kernel are highly recommended for use in this research because it can achieve 95% accuracy.

Wood Type Identification System using Naive Bayes Classification

Wood, a forest product and natural resource, is a raw material used to make household goods. Some types of wood have almost the same pattern or structure. Wood quality varies greatly depending on the tree species and the environmental conditions in which it grows. This makes it challenging to identify the type of wood, especially for wooden furniture users. Therefore, wood classification is essential to ensure that the wood used meets the required quality standards and requirements. Automatic classification of wood using image processing has several advantages and can make it easier for humans. One of the image processing methods for wood classification is the Naïve Bayes method. Feature extraction technique using GLCM using contrast, correlation, energy, and homogeneity attributes. The GLCM methods can be combined to create a system design to distinguish five wood species using an image-based wood type identification system. The study results have successfully designed a system to determine five types of wood using the framework of an image-based wood type identification system. An application system has been produced to distinguish five types of wood using the framework of an image-based wood type identification system with the GLCM feature extraction method and the Naive Bayes classification method. The application system successfully identified wood species with a test accuracy rate of 88%.

A Novel Fuzzy C-Means based Chameleon Swarm Algorithm for Segmentation and Progressive Neural Architecture Search for Plant Disease Classification

This study proposed a novel framework for plant leaf disease identification. The proposed model consists of four steps including pre-processing, segmentation, feature extraction, and classification. At first, the unwanted noise and overfitting are removed, and also image contrast level is enhanced. Secondly, the Fuzzy C-Means (FCM) based Chameleon Swarm Algorithm (CSA) named as (FCM-CSA) is used for plant leaf diseased part segmentation. In the third stage, the feature extraction is performed using a fast GLCM feature extraction model. Finally, the Progressive Neural Architecture Search (PNAS) is used for plant leaf disease identification. The experimental investigations are carried out using MATLAB software with the Mendeley database. From this dataset, we have used Apple Cedar Apple Rust (ACAR), Cherry Powdery Mildew (CPM), Corn Common Rust (CCCR), Apple Healthy (AH), Grape Black Rot (GBR), Pepper Bell Bacterial Spot (PBBS), Potato Late Blight (PLB) and Tomato Leaf Mold (TLM) disease images. Different measures such as precision, recall, sensitivity, specificity, and accuracy results are used to validate the performance of the proposed model.

Gerga Orange Quality Using Naïve Bayes Based on Feature Extraction

In an effort to increase the number of sales, the process of classifying the type of Gerga citrus fruit is very necessary. The problem that often occurs is the mixing of various types of fruit from the storage warehouse so that the quality of the fruit will be mixed and it will be difficult to determine the selling price because the quality of the fruit itself is not evenly distributed so that a sorting process is needed. There are still many sellers or growers of citrus fruits who sort the quality of the fruit manually so that it can take a very long time. Given these problems, it is necessary to classify the quality of Gerga oranges automatically with the Naïve Bayes Classifier algorithm with GLCM feature extraction and HSV color characteristics. as a method for classifying the quality of Gerga citrus fruit and as for the media used, there is digital image media. From the experiments that have been carried out the use of angles in the formation of co-occurrence matrices with the best accuracy values reaching 80% are found at angles of 0°, 45°, and 135°, while the lowest accuracy values are found at angles of 90°. It was concluded that the Gerga citrus fruit quality classification system using the Naïve Bayes method was categorized as good with an AUC value of 0.8.

Performance comparison of various machine learning classifiers using a fusion of LBP, intensity and GLCM feature extraction techniques for thyroid nodules classification

Performance comparison of various machine learning classifiers using a fusion of LBP, intensity and GLCM feature extraction techniques for thyroid nodules classification

FACIAL EMOTION DETECTION OF THERMAL AND DIGITAL IMAGES BASED ON MACHINE LEARNING TECHNIQUES

The aim of the study is (i) to determine temperature distribution for various emotions from the facial thermal images; (ii) to extract statistical features from the facial region using GLCM feature extraction technique and to classify the emotions using machine learning classifiers such as SVM and Naïve Bayes; (iii) to develop the custom CNN model for the classification of various emotions and compare its performance with machine learning classifiers. Fifty normal subjects were considered for the study to analyze the facial emotions using thermal and digital images. The four different emotions, such as happy, angry, neutral and sad, were obtained with a total image of 200 thermal and 200 digital images. Ten statistical features were extracted using the GLCM method from both thermal and digital images and fed into the machine learning classifiers. After data augmentation, the images are fed into the custom CNN model for the classification of various emotions. The SVM classifier produced an accuracy of 80% in thermal images and 76.5% in digital images compared to the Naive Bayes classifier. The developed CNN model improved the classification accuracy to 94.3% and 90.3% for thermal and digital image, respectively, for the multi-class classification of facial emotions. The CNN model implemented using thermal images provided better classification accuracy than digital images in facial emotion recognition. Hence, it was proved that thermal imaging techniques resulted in better performance in predicting facial emotion than digital images.

Mix histogram and gray level co-occurrence matrix to improve glaucoma prediction machine learning

Glaucoma is an eye disease that is the second leading cause of blindness. Examination of glaucoma by an ophthalmologist is usually done by observing the retinal image directly. Observations from one doctor to another may differ, depending on their educational background, experience, and psychological condition. Therefore, a glaucoma detection system based on digital image processing is needed. The detection or classification of glaucoma with digital image processing is strongly influenced by the feature extraction method, feature selection, and the type of features used. Many researchers have carried out various kinds of feature extraction for glaucoma detection systems whose accuracy needs to be improved. In general, there are two groups of features, namely morphological features and non-morphological features (image-based features). In this study, it is proposed to detect glaucoma using texture features, namely the GLCM feature extraction method, histograms, and the combined GLCM-histogram extraction method. The GLCM method uses 5 features and the Histogram uses 6 features. To distinguish between glaucoma and non-glaucoma eyes, the multi-layer perceptron (MLP) artificial neural network model serves as a classifier. The data used in this study consisted of 136 fundus images (66 normal images and 70 images affected by glaucoma). The performance obtained with this approach is an accuracy of 93.4%, a sensitivity of 86.6%, and a specificity of 100%.

Effect of Features and Angle on GLCM Feature Extraction on Accuracy for Object Classification

An autonomous car is a vehicle that can guide itself without human intervention. Various types of steeringless vehicles are being developed. The system of the future where computers take over the art of driving. The problem was before it became a concern in autonomous cars to get high safety. Autonomous cars need an early warning system to avoid accidents in front of the car, especially systems that can be used on highway locations. In this paper, we propose a vision-based vehicle detection system for vehicle detection in the form of cars. Our detection algorithm consists of two main components: Extraction of color features using GLCM values, and testing of 6 parameters of GLCM dissimilarity, correlation, homogeneity, contrast, ASM and energy. We use the SVM (Support Vector Machine) algorithm for the classification algorithm. The SVM (Support Vector Machine) classification in previous studies has had quite good results and has a fast computation time. Good accuracy results are found in the ASM feature and using an angle of 450

GLCM and CNN Deep Learning Model for Improved MRI Breast Tumors Detection

Breast cancer is one of the most common types of cancer among Iraqi women. MRI has been used in the detection of breast tumors for its efficient performance in the diagnosis process providing high accuracy. In this paper, breast MRI image data from 89 patients were classified using GLCM and CNN feature extraction methods. Four models were evaluated consisting of GLCM, CNN, combined GLCM and CNN features based models. The statistical ANOVA feature selection method was used to reduce the redundant features. The reduced feature subset was fed to CNN classifier for obtaining either normal or abnormal breast images. The proposed method was assessed in terms of accuracy, precision, recall and F1-score. The model provided 100% classification accuracy.

LOMBOK PEARL QUALITY CLASSIFICATION USING A COMBINATION OF FEATURE EXTRACTION AND ARTIFICIAL NEURAL NETWORKS BASED ON SHAPE

Lombok is attracted to the Moto GP event, which is held annually. Various tourism brands are owned by the island of Lombok, one of which is Mutiara. The ideal Pearl is perfectly round and smooth, but there are a variety of other shapes as well. One method that can be used to process Pearl's image is Computer Vision. For that, it is necessary to have a way to classify the quality of a Pearl based on its shape. The purpose of this study is to propose a system for pearl image classification by combining feature extraction with artificial neural networks. The method used in this study is GLCM feature extraction and Neural Networks. The proposed system can provide good classification results by combining the GLCM method and the Neural Network. This study uses Epochs 5, 10, 15, 30, 50, 100, 200, 300, and 500 with a learning rate of 0.5. The results of this study indicate that Epoch 100 gives the highest accuracy, 91.66%.

Classifying Lung Cancer as Benign and Malignant Nodule Using ANN of Back-Propagation Algorithm and GLCM Feature Extraction on Chest X-Ray Images

Classifying Lung Cancer as Benign and Malignant Nodule Using ANN of Back-Propagation Algorithm and GLCM Feature Extraction on Chest X-Ray Images

Classification of Guarantee Types Using Leaf Feature Extraction with Minutiae and GLCM Using K-NN Method

Indonesia is a fertile area that has a sub-tropical climate that makes plants grow well in various parts of Indonesia. There are various variants of guava in Indonesia. Of the several types have differences including the structure of the fruit, tree and leaves. The focus of this research is to classify guava species based on leaf bone image using GLCM feature extraction, minutiae and shape extraction using the K-NN method. In this study using a dataset of 4 types of guava as many as 300 images, where each type of as many as 75 images. In the extraction process to get the leaf bone image in this study, there are several processes, namely preprocessing, grayscale image, binary image and morphology then only get the leaf bone image. After getting the extracted value, then the data is processed using the K-NN method. The highest accuracy in the K-NN method is at k1 = 92.42% with a standard deviation of 6.05% (micro average: 92.45%). Thus GLCM feature extraction, minutiae and shape extraction can potentially increase the level of accuracy in guava classification based on leaf bone images.