Gray Level Co-occurence Matrix Research Articles

Klasifikasi Penyakit Tanaman Bawang Merah Menggunakan Metode SVM dan CNN

Shallots are one of the most widely produced crops in Enrekang Regency. The obstacle in cultivation is the presence of disease in the plant which can reduce production yields. We can recognize this disease from the spots on the leaves because these spots have unique color and texture characteristics. The aim of this research is to determine the results of the classification of shallot plant diseases which focuses on purple spot and moler disease. The classification algorithms used are CNN and SVM with RBF, linear, sigmoid and polynomial kernels. The feature extraction method used is Gray Level Co-occurance Matrix (GLCM). The analysis was carried out using 320 datasets with 2 classes, namely, purple spot disease and moler disease, each class has 160 datasets. The test results show that the CNN and SVM methods with RBF, linear and polynomial kernels get accuracy, precision, recall and F1 scores of 100% respectively. Meanwhile, the SVM method on the sigmoid kernel using texture feature extraction with the GLCM method states that the accuracy value is 75%, precision 75%, recall 73% and F1-Score 74%. So these results state that the Sigmoid method using GLCM feature extraction has the lowest value among other methods

Constructing the Schizophrenia Recognition Method Employing GLCM Features from Multiple Brain Regions and Machine Learning Techniques

Accurately diagnosing schizophrenia, a complex psychiatric disorder, is crucial for effectively managing the treatment process and methods. Various types of magnetic resonance (MR) images have the potential to serve as biomarkers for schizophrenia. The aim of this study is to numerically analyze differences in the textural characteristics that may occur in the bilateral amygdala, caudate, pallidum, putamen, and thalamus regions of the brain between individuals with schizophrenia and healthy controls via structural MR images. Towards this aim, Gray Level Co-occurence Matrix (GLCM) features obtained from five regions of the right, left, and bilateral brain were classified using machine learning methods. In addition, it was analyzed in which hemisphere these features were more distinctive and which method among Adaboost, Gradient Boost, eXtreme Gradient Boosting, Random Forest, k-Nearest Neighbors, Linear Discriminant Analysis (LDA), and Naive Bayes had higher classification success. When the results were examined, it was demonstrated that the GLCM features of these five regions in the left hemisphere could be classified as having higher performance in schizophrenia compared to healthy individuals. Using the LDA algorithm, classification success was achieved with a 100% AUC, 94.4% accuracy, 92.31% sensitivity, 100% specificity, and an F1 score of 91.9% in healthy and schizophrenic individuals. Thus, it has been revealed that the textural characteristics of the five predetermined regions, instead of the whole brain, are an important indicator in identifying schizophrenia.

Value of [18F]FDG PET/CT radiomic parameters in the context of response to chemotherapy in advanced cervical cancer

The first-order statistical (FOS) and second-order texture analysis on basis of Gray-Level Co-occurence Matrix (GLCM) were obtained to assess metabolic, volumetric, statistical and radiomic parameters of cervical cancer in response to chemotherapy, recurrence and age of patients. The homogeneous group of 83 patients with histologically confirmed IIIC1–IVB stage cervical cancer were analyzed, retrospectively. Before and after chemotherapy, the advancement of the disease and the effectiveness of the therapy, respectively, were established using [18F] FDG PET/CT imaging. The statistically significant differences between pre- and post-therapy parameters were observed for SUVmax, SUVmean, TLG, MTV, asphericity (ASP, p = 0.000, Z > 0), entropy (E, p = 0.0000), correlation (COR, p = 0.0007), energy (En, p = 0.000) and homogeneity (H, p = 0.0018). Among the FOS parameters, moderate correlation was observed between pre-treatment coefficient of variation (COV) and patients’ recurrence (R = 0.34, p = 0.001). Among the GLCM textural parameters, moderate positive correlation was observed for post-treatment contrast (C) with the age of patients (R = 0.3, p = 0.0038) and strong and moderate correlation was observed in the case of En and H with chemotherapy response (R = 0.54 and R = 0.46, respectively). All correlations were statistically significant. This study indicates the remarkable importance of pre- and post-treatment [18F] FDG PET statistical and textural GLCM parameters according to prediction of recurrence and chemotherapy response of cervical cancer patients.

Based on CT radiomics model for predicting the response to first-line chemotherapy of diffuse large B-cell lymphoma

Objective: To investigate the potential value of CT Radiomics model in predicting the response to first-line chemotherapy in diffuse large B-cell lymphoma (DLBCL). Methods: Pre-treatment CT images and clinical data of DLBCL patients treated at Shanxi Cancer Hospital from January 2013 to May 2018 were retrospectively analyzed and divided into refractory patients (73 cases) and non-refractory patients (57 cases) according to the Lugano 2014 efficacy evaluation criteria. The least absolute shrinkage and selection operator (LASSO) regression algorithm, univariate and multivariate logistic regression analyses were used to screen out clinical factors and CT radiomics features associated with efficacy response, followed by radiomics model and nomogram model. Receiver operating characteristic (ROC) curve, calibration curve and clinical decision curve were used to evaluate the models in terms of the diagnostic efficacy, calibration and clinical value in predicting chemotherapy response. Results: Based on pre-chemotherapy CT images, 850 CT texture features were extracted from each patient, and 6 features highly correlated with the first-line chemotherapy effect of DLBCL were selected, including 1 first order feature, 1 gray level co-occurence matrix, 3 grey level dependence matrix, 1 neighboring grey tone difference matrix. Then, the corresponding radiomics model was established, whose ROC curves showed AUC values of 0.82 (95% CI: 0.76-0.89) and 0.73 (95% CI: 0.60-0.86) in the training and validation groups, respectively. The nomogram model, built by combining validated clinical factors (Ann Arbor stage, serum LDH level) and CT radiomics features, showed an AUC of 0.95 (95% CI: 0.90-0.99) and 0.91 (95% CI: 0.82-1.00) in the training group and the validation group, respectively, with significantly better diagnostic efficacy than that of the radiomics model. In addition, the calibration curve and clinical decision curve showed that the nomogram model had good consistency and high clinical value in the assessment of DLBCL efficacy. Conclusion: The nomogram model based on clinical factors and radiomics features shows potential clinical value in predicting the response to first-line chemotherapy of DLBCL patients.

Identifikasi Kematangan Buah Menggunakan Metode Gray Level Co-occurence Matrix pada Citra Digital

In the current era of information technology, the use of images is widely applied in various aspects of life. Some of the uses of image processing include the fields of military, medicine, education, agriculture and so on. One example of the use of image processing that will be discussed in this study is in the agricultural sector. Farmers can take advantage of technology in selecting fruit with the appropriate maturity level. In terms of selecting fruit based on the level of maturity, some fruit farmers still use the conventional method or with the human sense of sight, namely the eye. Therefore, this study was conducted for preliminary research that can change the conventional method into system that uses technology that makes a computerized way of identifying fruit maturity levels. The system that will be used to identify fruit maturity uses the Gray Level Co-occurance Matrix method on digital images and Euclidean Distance as a classification method. This application is built using MatLab 2019a programming. The test results show that the Gray Level Co-occurance Matrix and Euclidean Distance methods can be used to identify the ripeness of guava, oranges, bananas, papayas and mangoes into three categories, namely raw, unripe and ripe. The Gray Level Co-occurance Matrix and Euclidean Distance classification methods succeeded in identifying fruit maturity with an overall success rate of 87%.

Prediction of single pulmonary nodule growth by CT radiomics and clinical features - a one-year follow-up study.

With the development of imaging technology, an increasing number of pulmonary nodules have been found. Some pulmonary nodules may gradually grow and develop into lung cancer, while others may remain stable for many years. Accurately predicting the growth of pulmonary nodules in advance is of great clinical significance for early treatment. The purpose of this study was to establish a predictive model using radiomics and to study its value in predicting the growth of pulmonary nodules. According to the inclusion and exclusion criteria, 228 pulmonary nodules in 228 subjects were included in the study. During the one-year follow-up, 69 nodules grew larger, and 159 nodules remained stable. All the nodules were randomly divided into the training group and validation group in a proportion of 7:3. For the training data set, the t test, Chi-square test and Fisher exact test were used to analyze the sex, age and nodule location of the growth group and stable group. Two radiologists independently delineated the ROIs of the nodules to extract the radiomics characteristics using Pyradiomics. After dimension reduction by the LASSO algorithm, logistic regression analysis was performed on age and ten selected radiological features, and a prediction model was established and tested in the validation group. SVM, RF, MLP and AdaBoost models were also established, and the prediction effect was evaluated by ROC analysis. There was a significant difference in age between the growth group and the stable group (P < 0.05), but there was no significant difference in sex or nodule location (P > 0.05). The interclass correlation coefficients between the two observers were > 0.75. After dimension reduction by the LASSO algorithm, ten radiomic features were selected, including two shape-based features, one gray-level-cooccurence-matrix (GLCM), one first-order feature, one gray-level-run-length-matrix (GLRLM), three gray-level-dependence-matrix (GLDM) and two gray-level-size-zone-matrix (GLSZM). The logistic regression model combining age and radiomics features achieved an AUC of 0.87 and an accuracy of 0.82 in the training group and an AUC of 0.82 and an accuracy of 0.84 in the verification group for the prediction of nodule growth. For nonlinear models, in the training group, the AUCs of the SVM, RF, MLP and boost models were 0.95, 1.0, 1.0 and 1.0, respectively. In the validation group, the AUCs of the SVM, RF, MLP and boost models were 0.81, 0.77, 0.81, and 0.71, respectively. In this study, we established several machine learning models that can successfully predict the growth of pulmonary nodules within one year. The logistic regression model combining age and imaging parameters has the best accuracy and generalization. This model is very helpful for the early treatment of pulmonary nodules and has important clinical significance.

Identification of Banana Types with the Least-Squares Support Vector Machine

The classification of banana species is still done manually by banana farmers. This identification process has the disadvantage that it requires more manpower to sort, the level of perception of the type of fruit produced can be different because humans can experience fatigue, are not always consistent, and human judgments are also subjective. Thus, a tool is needed that can identify the type of banana fruit precisely and accurately. One of them is by creating a computer-based system using the statistical feature extraction method of digital images. By performing color feature extraction using Color Moments (RGBHSVYCbCr), then texture extraction using Gray-Level Co-occurence Matrix (GLCM), and using the Least-Squares Support Vector Machine (LS-SVM) method for classification of banana species. LS-SVM is a modification of SVM, which is used to improve classification performance. In the SVM algorithm, there is quadratic programming that is used to obtain the optimal solution in determining the Lagrange function; from the Lagrange function, it will be used in calculating the value of the weight and bias parameters. Quadratic programming is not efficient when applied to higher spatial dimensions because the computation will be very complex and very long. LS-SVM is better than standard SVM in terms of the calculation process, faster convergence, and higher precision. At the end of the experiment, the LS-SVM method succeeded in detecting the type of banana with a test accuracy value of 90%.

Identification of Malaria Parasite Patterns With Gray Level Co-Occurance Matrix Algorithm (GLCM)

The results of the test using 5 data of malaria parasite test imagery found that image 1 has an average accuracy value of the energy of 0.55627, homogeneity average of 0.8371, PSNR of 6.1336db, and MSE of 0.24358. Image 2 has an average energy accuracy value of 0.22274, an average Homonegity of 0.98532, a PSNR of 6.1336db, and an MSE of 0.24358. Image 3 has an energy average accuracy value of 0.28735, a Homonegity average accuracy value of 0.9793, a PSNR of 6.133db, and an MSE of 0.24358. Image 4 has an energy average accuracy value of 0.32907 and an average homogeneity accuracy value of 0.97073, PSNR 6.133db, and MSE 0.24358. Image 5 has an average accuracy value of 0.74102, Homonegity average of 0.99844, PSNR of 6.133db, and MSE of 0.4358. Image 6 has an accuracy value of 0.34758 energy, an average accuracy value of homogeneity of 0.99129, a PNSR of 6.133db, and an MSE of 0.24358. Obtained the rule if the average value of energy &gt; = 0.50 then the pattern of malaria parasites is very clear, namely Image 1 and image 5 with a pattern of malaria parasites is very clear.

Hand Image Based Skin Disease Identification Using Machine Learning and Deep Learning Algorithms

The skin disease is most commonly affected to all peoples. It is a big challenging process in skin image identification because of the combined texture shape with the color variations. This paper presented to identify a skin using texture features (Local Binary Pattern and Gray Level Co-occurance Matrix) and color features with various learning models. The proposed work technique for identification of skin disease has two stages: either extracted feature values and identification. During the first stage, texture features and color feature vector values are taken from the images in a dataset of hand skin database. During the second stage, the extracted feature vectors are trained by the various kinds of Machine Learning models. This hand skin image dataset also applied into Deep Learning model for identification. The performance measures of the system is evaluated using accuracy measure. The Convolutional Neural Network (CNN) model gives accuracy of 87.5% when compared to Machine Learning models.

Classification of Mint Leaf Types Based on the Image Using Euclidean Distance and K-Means Clustering with Shape and Texture Feature Extraction

Mint is a plant that has many benefits and uses. However, some people are not familiar with the types of mint leaves because they cannot tell the difference. Actually, if you look closely, mint leaves have their own characteristic shape and texture. However, most people judge mint leaves to have a shape similar to other leaves so it is difficult to tell them apart. This paper aims to classify the types of mint leaves using the Euclidean distance algorithm and K-Means clustering with shape and texture feature extraction. The K-Means Clustering Algorithm functions as a segmentation so that the image to be classified can be separated from other objects. In the feature extraction process, metric and eccentricity parameters are used. Meanwhile, for texture feature extraction, use the parameters in the Gray Level Co-occurence Matrix (GLCM). Furthermore, the classification process uses the Euclidean Distance algorithm which has a function to represent the level of similarity between two images by taking into account the distance value from the identified image. Based on the results of the evaluation using a confusion matrix by calculating precision, recall and accuracy, the precision value is 82%, recal is 84% ​​and accuracy is 83%.

Ekstraksi Fitur Tekstur dan Warna pada Kulit Katak Menggunakan GLCM dan Momen Warna

Anura is an order in the Amphibian class consisting of frogs and toads. Anura is very important in the ecosystem, especially its role as part of the food chain. Anura's main role is to maintain the balance of the ecosystem and as a bioindicator agent for changing environmental conditions such as water pollution, habitat destruction, disease and parasites, and climate change. This research applies digital image processing technology which is expected to assist in detecting types of frogs based on color and texture. This research uses 5 types of frogs, namely kongkang gading, kongkang poison, striped trees, small trees and flying trees with 20 images of each type of frog. This research uses the color feature extraction methods such Color Moment and texture extraction GLCM (Gray Level Co-occurance Matrix), then classified using K-Star. The results of the K-Star performance evaluation to classify the 5 types of frogs obtained the Accuracy (Acc) value of 0.93, Precision (Prec) of 0.94, Recall (Rec) of 0.93 and F-measure of 0.93. So that the classification results of frog species on texture and color feature extraction using the GLCM method and the Color Moment with the K-Star classification method have high performance and can work well.

Identifikasi Citra Tanaman Obat Jenis Rimpang dengan Euclidean Distance Berdasarkan Ciri Bentuk dan Tekstur

In the midst of the Covid-19 pandemic, increasing the body's immunity is very important. Some experts suggest consuming medicinal plants or herbs to boost immunity. In addition to being used as a cooking spice, this rhizome type plant turns out to have properties and benefits for health, especially to increase immunity. However, many people do not know and it is difficult to distinguish the type of rhizome plant. This type of rhizome plant can be identified based on the characteristics seen from the shape and texture. However, most people judge the type of rhizome has a shape that is difficult to distinguish. This study aims to determine the type of medicinal plant rhizome with Euclidean distance and extraction of shape and texture. Extraction of shape features using metric and eccentricity parameters. This parameter is considered to be able to recognize shape objects and can distinguish them from other objects. Meanwhile, texture feature extraction uses Gray Level Co-occurence Matrix (GLCM) with contrast, correlation, energy, and homogeneity parameters. For the identification process, Euclidean distance is used which serves to represent the level of two images that consider the distance value from Euclidean. From the results of the evaluation using a confusion matrix by calculating precision, recall and accuracy, it gets a precision value of 83%, recal 87% and an accuracy of 85%. These results indicate that the Euclidean distance and extraction of shape and texture features can identify the object image of medicinal plants with rhizome types well

Tumor Categorization Model (TCM) Using Soft Computing Techniques for Providing Efficient Medical Support in Brain Tumor Treatments

Brain cancer identification and segmentation is a prolonged and difficult task in Medical Image Processing, which is most significant for providing appropriate treatment and increase patient’s life span. With the advancements available in medical fields, soft computing techniques are incorporated to accurate detection and classification of brain tumors. Besides brain cancer detection, it is vital to categorize tumor stage based on their features. For that concern, this paper develops a Tumor Categorization Model (TCM) that includes image processing and soft computing techniques. Here, pre-processing is carried out using modified Gabor filter and segmentation process is performed with OTSU thresholding. Following segmentation, region growing is processed based on the pixel intensities of input MRI brain images. Further, Discrete Wavelet Transform is enforced for extorting image features as well as gray-level co-occurence matrix features are also derived for appropriate classifications. Finally, the input MRI images are classified using Boosting Support Vector Machine (BSVM) with the benchmark dataset called DICOM and BraTS dataset. The experimental results demonstrate accurate brain tumor detection and categorization by the efficient incorporation of image processing and soft computing methodologies, provides efficient clinical support in providing treatments.

Pengenalan Pola Tulisan Tangan Aksara Bima menggunakan Ciri Tekstur dan KNN

As the fact, that is Bima script did not familiar to bimanese, Bima script as a cultural heritage needs to be preserved. Pattern recognition has been used to recognize several of ancient script. Gray Level Co-occurence Matrix (GLCM) as features exctraction and K Nearest Neighbour (KNN) as a classifier show the good performance to recognize of an ancient script. so, in this research we use GLCM and KNN to recognize Bima script. we use 2640 images of handwritting bima Script that is collected from 10 volunter. Each volunter write 22 of Bima script twelve times each script. The experimental result show that the performance of our model is good enough, with 60.86% of accuracy that is obtained by manhattan distances.

Iris-based Image Processing for Cholesterol Level Detection using Gray Level Co-Occurrence Matrix and Support Vector Machine

Serious illnesses such as strokes and heart attacks can be triggered by high levels of cholesterol in human blood that exceeds ideal conditions, where the ideal cholesterol level is below 200 mg/dL. To find out cholesterol levels need a long process because the patient must go through a blood sugar test that requires the patient to undergo fasting for 10–12 hours first before the test. Iridology is a branch of science that studies human iris and its relation to the wellness of human internal organs. The method can be used as an alternative for medical analysis. Iridology thus can be used to assess the conditions of organs, body construction, and other psychological conditions. This paper proposes a cholesterol detection system based on the iris image processing using Gray Level Co-Occurrence Matrix (GLCM) and Support Vector Machine (SVM). GLCM is used as the feature extraction method of the image, while SVM acts as the classifier of the features. In addition to GLCM and SVM, this paper also construct a preprocessing method which consist of image resizing, segmentation, and color image to gray level conversion of the iris image. These steps are necessary before the GLCM feature extraction step can be applied. In principle, the GLCM method is a construction of a matrix containing the information about the proximity position of gray level images pixels. The output of GLCM is fed to the SVM that relies on the best hyperplane. Thus, SVM performs as a separator of two data classes of the input space. From the simulation results, the system built was able to detect excess cholesterol levels through iris image and classify into three classes, namely: non–cholesterol (&lt; 200 ), risk of cholesterol (200–239 ) and high cholesterol (&gt; 240 ). The accuracy rate obtained was 94.67% with an average computation time of 0.0696 . It was using each of the 75 training and test data, with the second-order parameters used are contrast–correlation–energy–homogeneity, pixel distance = 1, quantization level = 8, Polynomial kernel types and One Against One Multiclass. Iris has specific advantages which can record all organ conditions, body construction and psychological conditions. Therefore, Iridology as a science based on the arrangement of iris fibers can be an alternative for medical analysis. In this paper proposes a cholesterol detection system through the iris using Gray Level Co-occurence Matrix and Support Vector Machine. Input system is an iris image that will be processed by pre-processing and then extracted features with the Gray Level Co-Occurrence Matrix method which is a matrix containing information about position the proximity of pixels that have a certain gray level. And then classified with the Support Vector Machine method that relies on the best hyper lane which functions as a separator of two data classes in the input space. From the simulation results, the system built was able to detect excess cholesterol levels through iris image and classify into three classes are: risk of cholesterol, high cholesterol and non–cholesterol with an accuracy rate of 96.47% and average computation time was 0.0696 using each of the 75 training and test data, with the second-order parameters used are contrast–correlation–energy–homogeneity, pixel distance = 1, quantization level = 8, Polynomial kernel types and One Against One Multiclass.

Wavelet Transform and Texton based Analysis for Detection of Benign and Malignant Masses.

Cancer has affected the human community to a large extent due to its low survival rate towards the end stage of the disease. It is asymptomatic in many cases during the initial stage. Thus the dependency on early diagnosis and regular check up increases manifold. Computer Aided Diagnostic Model is the need of the hour which will increase the diagnostic efficiency. A total of 400 images acquired from the Digital Database for Screening Mammography have been used here for analysis. This paper proposes a novel technique to differentiate benign and malignant breast lesions in mammograms using multiresolution analysis and Schmid Filter Bank, which were not reported earlier. A three level Haar wavelet decomposed image(L1, L2, L3) is obtained for each Region of Interest. In each level Texton based analysis is further investigated through Schmid filter bank. Statistical features and Haralick's Features are obtained from filter response and Gray Level Cooccurence Matrix respectively. Partition Membership Filter is further applied to the feature matrix for feature partitioning. The method shows maximum accuracy of 98.63% and Area under Curve of 0.981 using Random Forest Classifier and ten fold cross validation.

Detection of Cholesterol Levels by Analyzing Iris Patterns using Backpropagation Neural Network

Detecting cholesterol levels with iridology can be an alternative method for checking human’s health. Iridology analyzes diseases and weaknesses of the body based on the shape and structure of the iris. This study uses image processing to analyze patterns in the outer portion of the iris bordering the sclera. Colored iris images are converted to grayscale to facilitate image processing. The results of color conversion still contain noise so that the Median Filter is used to eliminate noise in the image. The iris image which is still in the form of polar is transformed into a rectangular shape. This is used to facilitate the taking of the area to be analyzed. Next, the iris image is filtered using a Gaussian Filter to get smooth results. This is used to remove lines on the iris image after being converted into a rectangular shape. From the filtered image, the statistical value is calculated using the Gray Level Co-Occurance Matrix (GLCM). This is a comparison method which will produce several statistical characteristics, namely Energy, Correlation, Contrast, and Homogeneity. The four statistical characteristics will be used as input data for training using the Backpropagation Neural Network method that will produce output in the form of normal cholesterol or high cholesterol. The results of experiments on thirty images obtained an accuracy of 96.67%.

Effect Percentage of Seam Carving Implementation on Image Based on Texture Characteristics Using GLCM

flexibility of the appearance of electronic devices currently causes new demands on digital media, especially for the provision of content. Until now, images, although an important element in digital media, usually remain rigid in terms of size and cannot change size so that they cannot adjust to different layouts automatically. The solution to overcome this problem is by resizing the image. One method that works well for resizing images is seam carving. Seam carving aims to resize the image by not eliminating important content in the image. This study aims to see the effect of changing the texture of seam carving images to images that have different levels of complexity. The trial was conducted by comparing the original image and seam carving image. Seam carving image used is differentiated based on the ratio scale, which is 10%, 20%, 30%, 40%, 50%. Testing is done by processing 4 image samples using the Gray Level Co-Occurence Matrix (GLCM) method including Contrast, Correlation, Energy and Homogeneity. Then testing is done by comparing the original image with seam carving image using the features of GLCM. From the test results it can be concluded that seam carving as a method of resizing images can work well, with the comparison of textures using GLCM the highest level of similarity reaches 98% and the lowest is 86%.

Segmentation and Validation of Liver Tumors

Automatic detection, extraction and validation of tumors from the segmented Computed Tomography (CT) liver is a crucial task. Segmentation of tumors provides a landmark to detect and extract tumors from segmented liver image. In this proposed work, Otsu thresholding technique, Level set method, Super pixel-Overlay method and Fast Marching Method (FMM) are used to extract the tumors. Later, Texture features are extracted from the segmented tumors using Gray Level Co-Occurance Matrix (GLCM) and these tumors are validated using Euclidean distance. The work is evaluated on 3DircadB and Clumax dataset using Accuracy, False Positive and Overlap Error parameters. Self relative study and empirical comparative study are performed and results are tabulated. The observation is that Fast Marching method has performed better than existing methods.

Identifikasi Tulisan Tangan Huruf Katakana Jepang Dengan Metode Euclidean

Katakana is one of the traditional Japanese letters used to absorption words from other languanges. In the inttroduction of an object a learning process is needed, which is obtained through the characteristics and experience of observing similar objects after being acquired. But manually it is quite difficult to distinguish between 5 hiragana vowels starting from the image data acquisition process, image processing, feature extraction using Gray Level Co-occurance Matrix (GLCM) while classifiers use the euclidean distance method. The results of the tests carried out showed an accuracy rate of around 78% using the euclidean method.