Classification of Chili Leaf Disease Using the Gray Level Co-occurrence Matrix (GLCM) and the Support Vector Machine (SVM) Methods

2021 ◽  
Author(s):  
Yuslena Sari ◽  
Andreyan Rizky Baskara ◽  
Rika Wahyuni
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Gray Level ◽  
Leaf Disease ◽  
Occurrence Matrix

scholarly journals Digit Classification of Majapahit Relic Inscription using GLCM-SVM

2018 ◽  
Vol 1 (2) ◽  
pp. 46
Author(s):  
Tri Septianto ◽  
Endang Setyati ◽  
Joan Santoso
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Template Matching ◽  
Support Vector ◽  
Gray Level ◽  
Class 1 ◽  
Occurrence Matrix ◽  
Handwritten Recognition

A higher level of image processing usually contains some kind of classification or recognition. Digit classification is an important subfield in handwritten recognition. Handwritten digits are characterized by large variations so template matching, in general, is inefficient and low in accuracy. In this paper, we propose the classification of the digit of the year of a relic inscription in the Kingdom of Majapahit using Support Vector Machine (SVM). This method is able to cope with very large feature dimensions and without reducing existing features extraction. While the method used for feature extraction using the Gray-Level Co-Occurrence Matrix (GLCM), special for texture analysis. This experiment is divided into 10 classification class, namely: class 1, 2, 3, 4, 5, 6, 7, 8, 9, and class 0. Each class is tested with 10 data so that the whole data testing are 100 data number year. The use of GLCM and SVM methods have obtained an average of classification results about 77 %.

scholarly journals CLASSIFICATION OF PNEUMONIA USING SUPPORT VECTOR MACHINE

2020 ◽  
Vol 1 (1) ◽  
pp. 21-32
Author(s):  
Risha Ambar Wati ◽  
Hafiz Irsyad ◽  
Muhammad Ezar Al Rivan
Keyword(s):  
Support Vector Machine ◽  
Lung Disease ◽  
Extraction Method ◽  
Classification Method ◽  
Support Vector ◽  
Gray Level ◽  
X Rays ◽  
X Ray ◽  
Contrast Stretching

Pneumonia is a type of lung disease caused by bacteria, viruses, fungi, or parasites. One way to find out pneumonia is by x-ray. X-rays will be analyzed to determine whether there is pneumonia or not. This study aims to classify the x-ray results whether there is pneumonia or not on the x-ray results. The classification method used in this study were Support Vector Machine (SVM) and Gray Level Co-Occurrence (GLCM) for the extraction method. There are several stages before classification, namely cropping, resizing, contrast stretching, and thresholding then extracted using GLCM and classified using SVM. The results showed that the best accuracy of 62.66%.

scholarly journals COMPARISON OF THE INFLUENCE OF STANDARDIZATION AND NORMALIZATION OF DATA ON THE EFFECTIVENESS OF SPONGY TISSUE TEXTURE CLASSIFICATION

2019 ◽  
Vol 9 (3) ◽  
pp. 66-69
Author(s):  
Róża Dzierżak
Keyword(s):  
Support Vector Machine ◽  
Texture Classification ◽  
High Accuracy ◽  
Support Vector ◽  
Multilayer Perceptrons ◽  
Bayes Classifier ◽  
Spongy Tissue ◽  
Importance Ranking ◽  
Occurrence Matrix

The aim of this article was to compare the influence of the data pre-processing methods – normalization and standardization – on the results of the classification of spongy tissue images. Four hundred CT images of the spine (L1 vertebra) were used for the analysis. The images were obtained from fifty healthy patients and fifty patients with diagnosed with osteoporosis. The samples of tissue (50×50 pixels) were subjected to a texture analysis to obtain descriptors of features based on a histogram of grey levels, gradient, run length matrix, co-occurrence matrix, autoregressive model and wavelet transform. The obtained results were set in the importance ranking (from the most important to the least important), and the first fifty features were used for further experiments. These data were normalized and standardized and then classified using five different methods: naive Bayes classifier, support vector machine, multilayer perceptrons, random forest and classification via regression. The best results were obtained for standardized data and classified by using multilayer perceptrons. This algorithm allowed for obtaining high accuracy of classification at the level of 94.25%.

KLASIFIKASI TUMOR JINAK DAN TUMOR GANAS PADA CITRA MAMMOGRAM MENGGUNAKAN GRAY LEVEL CO-OCCURRENCE MATRIX (GLCM) DAN SUPPORT VECTOR MACHINE (SVM)

2021 ◽  
Vol 26 (2) ◽  
pp. 176-186
Author(s):  
Lulu Mawaddah Wisudawati
Keyword(s):  
Support Vector Machine ◽  
World Health Organization ◽  
Computer Aided Diagnosis ◽  
World Health ◽  
Support Vector ◽  
Gray Level ◽  
Computer Aided ◽  
Health Organization ◽  
Occurrence Matrix ◽  
Aided Diagnosis

Kanker payudara merupakan penyebab utama kematian pada wanita. Data Global Cancer Observatory 2018 dari World Health Organization (WHO, 2020) menunjukkan kasus kanker yang paling banyak terjadi di Indonesia adalah kanker payudara, yakni 58.256 kasus atau 16.7% dari total 348.809 kasus kanker. Mamografi merupakan teknik yang paling umum digunakan dalam mendeteksi tumor payudara menggunakan sistem sinar-X dosis rendah. Ada beberapa tipe abnormalitas dalam citra mammogram, yaitu mikrokalsifikasi dan massa. Penelitian ini bertujuan untuk meningkatkan performa sistem Computer-Aided Diagnosis (CAD) dalam mengklasifikasi tumor jinak dan tumor ganas dengan mengembangkan metode ekstraksi fitur menggunakan Gray Level Co-Occurrence Matrix (GLCM) dan metode klasifikasi menggunakan Support Vector Machine (SVM). Uji coba dilakukan dengan menggunakan database DDSM dengan 256 citra abnormal (95 tumor jinak dan 161 tumor ganas) menghasilkan nilai akurasi sebesar 83.59% dengan nilai sensitivitas dan spesifisitas 87.58% dan 76.84%. Selain itu, didapatkan nilai AUC sebesar 0.98%. Metode tersebut menunjukkan bahwa sistem memberikan hasil performa yang baik dalam mengklasifikasi tumor jinak dan tumor ganas.

Static Signature Verification Based on Texture Analysis Using Support Vector Machine

2017 ◽  
Vol 8 (2) ◽  
pp. 22-32 ◽  
Author(s):  
Subhash Chandra ◽  
Sushila Maheshkar
Keyword(s):  
Support Vector Machine ◽  
Texture Features ◽  
Signature Verification ◽  
Support Vector ◽  
Gray Level ◽  
Global Level ◽  
First Order ◽  
Svm Model ◽  
Level Information ◽  
Occurrence Matrix

Off-line hand written signature verification performs at the global level of image. It processes the gray level information in the image using statistical texture features. The textures and co-occurrence matrix are analyzed for features extraction. A first order histogram is also processed to reduce different writing ink pens used by signers. Samples of signature are trained with SVM model where random and skilled forgeries have been used for testing. Experimental results are performed on two databases: MCYT-75 and GPDS Synthetic Signature Corpus.

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