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:10.1109/eecsi.2018.8752726

Cited by 28 publications

(11 citation statements)

References 3 publications

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“…6. The proposed framework is more compelling and proficient than different frameworks, considered in previous works, to show the signs of improvement comprehension of the exhibition of our model we have compared with the previous work of others, for instance, with [8]- [11]. In [8], the accuracy for the best case is seen as 92% while the accuracy for the current technique was 98%.…”

Section: Discussionmentioning

confidence: 56%

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Facile Diabetic Retinopathy Detection using MRHE-FEED and Classification using Deep Convolutional Neural Network

Zubair¹

2021

Preprint

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<div><div><div><p>Diabetic Retinopathy (DR) is an intricacy of dia- betes that affects the eyes. In this paper, we have proposed a hybrid pre-processing and feature extraction technique named as Microaneurysm Retinal vein Haemorrhage Exudate (MRHE) extraction using Feature Enhancement and Edge Detection (FEED) which can extract all the features in a single step and with very less complexity. To classify the presence of DR, we have used an efficient Deep Convolutional Neural Network (D-CNN), model. The D-CNN model is trained with four salient features namely retinal veins, MA’s, exudates, and haemorrhages which were extracted from the raw images using image-processing techniques. After training and testing the D-CNN model, we were able to classify the presence of DR based on the features extracted from the testing data. To implement this proposed method, we have used a dataset from the STructured Analysis of the Retina (STARE) Database, which comprises of retinal images taken under various imaging conditions using fundus photography. To demonstrate the legitimacy of the proposed method, we have compared our method with the existing DR detection and classification methods such as SVM, ANN,etc.. Performance evaluation results in terms of Accuracy and Recall show that the proposed algorithm outperforms other existing DR classification methods.</p></div></div></div>

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Section: Discussionmentioning

confidence: 56%

“…As compared by the information given above the current technique seems, by all accounts, to be giving more accurate output. The parameters used for evaluating the classifiers in this study are sensitivity, and accuracy as can be calculated by equation (11) The ROC curve which is plotted with TPR against FPR is shown in Fig. 8 .…”

Section: Discussionmentioning

confidence: 99%

Facile Diabetic Retinopathy Detection using MRHE-FEED and Classification using Deep Convolutional Neural Network

Zubair¹

2021

Preprint

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“…This feature will be used in the classification stage to introduce the input unit to the target output to be easier in the classification stage [27]. In this research, feature extraction process compares HOG method [28]- [30], GLCM [31]- [34], and shape feature extraction [35]- [37]. In this research, GLCM uses four parameters, namely contrast, correlation, energy, and homogeneity.…”

Section: B Feature Extractionmentioning

confidence: 99%

Comparison of the histogram of oriented gradient, GLCM, and shape feature extraction methods for breast cancer classification using SVM

Mu'jizah

Novitasari

2021

J. Teknol. dan Sist. Komput

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Breast cancer originates from the ducts or lobules of the breast and is the second leading cause of death after cervical cancer. Therefore, early breast cancer screening is required, one of which is mammography. Mammography images can be automatically identified using Computer-Aided Diagnosis by leveraging machine learning classifications. This study analyzes the Support Vector Machine (SVM) in classifying breast cancer. It compares the performance of three features extraction methods used in SVM, namely Histogram of Oriented Gradient (HOG), GLCM, and shape feature extraction. The dataset consists of 320 mammogram image data from MIAS containing 203 normal images and 117 abnormal images. Each extraction method used three kernels, namely Linear, Gaussian, and Polynomial. The shape feature extraction-SVM using Linear kernel shows the best performance with an accuracy of 98.44 %, sensitivity of 100 %, and specificity of 97.50 %.

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“…Many research articles have been published on detecting DR utilizing fundus images and ML methods in recent years [ 15 , 16 , 17 , 18 , 19 ]. In some investigations, the researchers used binary fundus image datasets, and the rest used multiclass datasets.…”

Section: Introductionmentioning

confidence: 99%

Automatic Screening of Diabetic Retinopathy Using Fundus Images and Machine Learning Algorithms

2022

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Diabetic Retinopathy is a vision impairment caused by blood vessel degeneration in the retina. It is becoming more widespread as it is linked to diabetes. Diabetic retinopathy can lead to blindness. Early detection of diabetic retinopathy by an ophthalmologist can help avoid vision loss and other complications. Diabetic retinopathy is currently diagnosed by visually recognizing irregularities on fundus pictures. This procedure, however, necessitates the use of ophthalmic imaging technologies to acquire fundus images as well as a detailed visual analysis of the stored photos, resulting in a costly and time-consuming diagnosis. The fundamental goal of this project is to create an easy-to-use machine learning model tool that can accurately predict diabetic retinopathy using pre-recorded digital fundus images. To create the suggested classifier model, we gathered annotated fundus images from publicly accessible data repositories and used two machine learning methods, support vector machine (SVM) and deep neural network (DNN). On test data, the proposed SVM model had a mean area under the receiver operating characteristic curve (AUC) of 97.11%, whereas the DNN model had a mean area AUC of 99.15%.

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

Cited by 28 publications

References 3 publications

Facile Diabetic Retinopathy Detection using MRHE-FEED and Classification using Deep Convolutional Neural Network

Facile Diabetic Retinopathy Detection using MRHE-FEED and Classification using Deep Convolutional Neural Network

Comparison of the histogram of oriented gradient, GLCM, and shape feature extraction methods for breast cancer classification using SVM

Automatic Screening of Diabetic Retinopathy Using Fundus Images and Machine Learning Algorithms

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