Automated Diagnosis System of Diabetic Retinopathy Using GLCM Method and SVM Classifier

Foeady, Ahmad Zoebad; Novitasari, Dian Candra Rini; Asyhar, Ahmad Hanif; Firmansjah, Muhammad

doi:10.11591/eecsi.v5.1630

Cited by 9 publications

(11 citation statements)

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“…Gray Level Co-occurrence Matrix (GLCM) is a very popular feature-based feature extraction method [18][19]. GLCM determines the texture relationship between pixels that have the same degree of grayness so that statistical features or features are obtained in an image [20].…”

Section: Gray Level Co-occurrence Matrix (Glcm)mentioning

confidence: 99%

“…Whereas, FP (False Positive) is a class of actually negative but predicted positive, so class labels are predicted wrong, and FN (False Negative) is a class of actually positive but predicted negative, so class labels are predicted wrong [30]. This can be seen in Table 1 as follows: From Table 2.1, the level of accuracy, sensitivity, and specificity of an algorithm model can be calculated using Equation 8, 9, and 10 [18] .…”

Section: Confusion Matrixmentioning

confidence: 99%

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Image X-Ray Classification for COVID-19 Detection Using GCLM-ELM

2021

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COVID-19 is a disease or virus that has recently spread worldwide. The disease has also taken many casualties because the virus is notoriously deadly. An examination can be carried out using a chest X-Ray because it costs cheaper compared to swab and PCR tests. The data used in this study was chest X-Ray image data. Chest X-Ray images can be identified using Computer-Aided Diagnosis by utilizing machine learning classification. The first step was the preprocessing stage and feature extraction using the Gray Level Co-Occurrence Matrix (GLCM). The result of the feature extraction was then used at the classification stage. The classification process used was Extreme Learning Machine (ELM). Extreme Learning Machine (ELM) is one of the artificial neural networks with advanced feedforward which has one hidden layer called Single Hidden Layer Feedforward Neural Networks (SLFNs). The results obtained by GLCM feature extraction and classification using ELM achieved the best accuracy of 91.21%, the sensitivity of 100%, and the specificity of 91% at 135° rotation using linear activation function with 15 hidden nodes.

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Section: Gray Level Co-occurrence Matrix (Glcm)mentioning

confidence: 99%

Section: Confusion Matrixmentioning

confidence: 99%

Image X-Ray Classification for COVID-19 Detection Using GCLM-ELM

2021

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“…The confusion matrix is n x n in size, where n is the number of different classes [35]. The confirmation matrix can determine the accuracy, recall, and specificity obtained from the values of several parameters, such as True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (FN) [36,37]. The confusion matrix table is shown in Table 3.…”

Section: Experiments Scenariomentioning

confidence: 99%

Two Stages Outlier Removal as Pre-processing Digitizer Data on Fine Motor Skills (FMS) Classification Using Covariance Estimator and Isolation Forest

Fanani¹

2021

IJIES

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The increase of the classification accuracy level has become an important problem in machine learning especially in diverse data-set that contain the outlier data. In the data stream or the data from sensor readings that produce large data, it allows a lot of noise to occur. It makes the performance of the machine learning model is disrupted or even decreased. Therefore, clean data from noise is needed to obtain good accuracy and to improve the performance of the machine learning model. This research proposes a two-stages for detecting and removing outlier data by using the covariance estimator and isolation forest methods as pre-processing in the classification process to determine fine motor skill (FMS). The dataset was generated from the process of recording data directly during cursive writing by using a digitizer. The data included the relative position of the stylus on the digitizer board. x position, y position, z position, and pressure values are then used as features in the classification process. In the process of observation and recording, the generated data was very huge so some of them produce the outlier data. From the experimental results that have been implemented, the level of accuracy in the FMS classification process increases between 0.5-1% by using the Random Forest classifier after the detection and outlier removal by using covariance estimator and isolation forest. The highest accuracy rate achieves 98.05% compared to the accuracy without outlier removal, which is only about 97.3%.

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“…In addition, various studies have also used various methods in the classification of mammogram images, for example, the Backpropagation method [15], [16], Support Vector Machine [6], [17], [18], Fuzzy Neural Network [19], and Adaptive Neuro-Fuzzy Inference System (ANFIS) [19]- [24], etc. Some of these methods are subcategories of artificial neural network methods that have been widely implemented for various types of diseases by previous researchers [21].…”

Section: Introductionmentioning

confidence: 99%

Application of Feature Extraction for Breast Cancer using One Order Statistic, GLCM, GLRLM, and GLDM

Novitasari¹,

Lubab²,

Sawiji³

et al. 2019

Adv. sci. technol. eng. syst. j.

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The increasing number of breast cancer in recent years has attracted numerous researchers' attention. Several techniques of Computer Aided Diagnosis System have been proposed as alternative solutions to diagnose breast cancer. The flaw of simply using the naked eye to see the differences between normal and with cancer mammogram images makes the texture analysis play an important role in classifying breast cancer. In this study, the results of the classification were compared using various methods of texture analysis in extracting a feature of the mammogram image. Some texture analysis methods, including first order, which consist of GLCM, GLRLM, and GLDM, have successfully extracted features based on their characteristics. The statistical features of these methods are used as input for the ECOC SVM classification, which three kernel comparisons; linear, RBF, and polynomial, build the classification. The results show that the best kernel is polynomial kernels with statistical features built by GLRLM with 93.9757% accuracy value.

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Automated Diagnosis System of Diabetic Retinopathy Using GLCM Method and SVM Classifier

Cited by 9 publications

References 0 publications

Image X-Ray Classification for COVID-19 Detection Using GCLM-ELM

Image X-Ray Classification for COVID-19 Detection Using GCLM-ELM

Two Stages Outlier Removal as Pre-processing Digitizer Data on Fine Motor Skills (FMS) Classification Using Covariance Estimator and Isolation Forest

Application of Feature Extraction for Breast Cancer using One Order Statistic, GLCM, GLRLM, and GLDM

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