Classification of non-proliferative diabetic retinopathy based on hard exudates using soft margin SVM

Tjandrasa, Handayani; Putra, Ricky Eka; Wijaya, Arya Yudhi; Arieshanti, Isye

doi:10.1109/iccsce.2013.6719993

Cited by 42 publications

(13 citation statements)

References 9 publications

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“…In [8] have proposed a classification system to classify two severity levels namely moderate NPDR and severe NPDR. The proposed system has three stages: the first stage is exudate segmentation based on the morphology method, the features from the segmented image are extracted in the second stage and classified into different severity levels using soft margin SVM in the last stage.…”

Section: Related Workmentioning

confidence: 99%

An automated grading system for diabetic retinopathy using curvelet transform and hierarchical classification

Mukti¹,

Eswaran²,

Hashim³

et al. 2018

IJET

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In this paper, an automated system for grading the severity level of Diabetic Retinopathy (DR) disease based on fundus images is presented. Features are extracted using fast discrete curvelet transform. These features are applied to hierarchical support vector machine (SVM) classifier to obtain four types of grading levels, namely, normal, mild, moderate and severe. These grading levels are determined based on the number of anomalies such as microaneurysms, hard exudates and haemorrhages that are present in the fundus image. The performance of the proposed system is evaluated using fundus images from the Messidor database. Experiment results show that the proposed system can achieve an accuracy rate of 86.23%.

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Section: Related Workmentioning

confidence: 99%

An automated grading system for diabetic retinopathy using curvelet transform and hierarchical classification

Mukti¹,

Eswaran²,

Hashim³

et al. 2018

IJET

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“…NPDR is the early stage of DR and if the patient blood sugar is uncontrolled, it will rise to PDR. NPDR is characterized by the appearance of deposits of cholesterol (exudates) in the retina can be later be developed into bleeding (hemorraghe) [13][14][15]17]. However, early diagnosis and treatment from NPDR can prevent PDR from damaging the patient's vision [13].…”

Section: Diabetes and Diabetic Retinopathy (Dr)mentioning

confidence: 99%

Classification of visualization exudates fundus images results using support vector machine

Hassan¹,

Tahir²,

Zabidi³

et al. 2018

J. Fundam and Appl Sci.

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This paper classifies the characteristics of normal and exudates fundus images by determine its accuracy for diagnostic purposes.images (81 normal and 68 exudates) from MESSIDOR databas the fundus images. The OD removed fundus image and fundus image with the exudates areas removed. The SVM1 classifier was applied to 30 test fundus images to determine the best optimal parameter. The kernel function settings an effect on the classification results. For SVM1, the best parameter in classifying pixels is linear kernel function. The visualization results using CAC and radar chart are classified using SVM2 to determine its accuracy. pixels in fundus image using linear kernel function of SVM1 to diagnose DR. an effect on the classification results. For SVM1, the best parameter in classifying pixels is linear kernel function. The visualization results using CAC and radar chart are classified using has proven to discriminated exudates and non exudates pixels in fundus image using linear kernel function of SVM1 to diagnose DR.Diabetic retinopathy (DR); Optic disc (OD); Support Vector Machine (SVM);

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“…Retinopati diabetes adalah suatu mikroangiopati progresif yang ditandai oleh kerusakan dan sumbatan pada pembuluh darah halus retina. Kelainan patologik yang paling dini adalah penebalan membran basal endotel kapiler dan penurunan jumlah perisit (Tjandrasa, 2013) .…”

Section: Diabetic Retinopathyunclassified

Analisis Perbandingan KNN dengan SVM untuk Klasifikasi Penyakit Diabetes Retinopati berdasarkan Citra Eksudat dan Mikroaneurisma

2015

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ABSTRAKPenelitian mengenai pengklasifikasian tingkat keparahan penyakit Diabetes Retinopati berbasis image processing masih hangat dibicarakan, citra yang biasa digunakan untuk mendeteksi jenis penyakit ini adalah citra optik disk, mikroaneurisma, eksudat, dan hemorrhages yang berasal dari citra fundus. Pada penelitian ini telah dilakukan perbandingan algoritma SVM dengan KNN untuk klasifikasi penyakit diabetes retinopati (mild, moderate, severe) berdasarkan citra eksudat dan microaneurisma. Untuk proses ekstraksi ciri digunakan metode wavelet pada masing-masing kedua metode tersebut. Pada penelitian ini digunakan 160 data uji, masing-masing 40 citra untuk kelas normal, kelas mild, kelas moderate, kelas saviere. Tingkat akurasi yang diperoleh dengan menggunakan metode KNN lebih tinggi dibandingkan SVM, yaitu 65 % dan 62%. Klasifikasi dengan algoritma KNN diperoleh hasil terbaik dengan parameter K=9 cityblock. Sedangkan klasifikasi dengan metode SVM diperoleh hasil terbaik dengan parameter One Agains All.Kata kunci: Diabetic Retinopathy, KNN , SVM, Wavelet. ABSTRACT Research based on severity classification of the disease diabetic retinopathy by using image processing method is still hotly debated, the image is used to detect the type of this disease is an optical image of the disk, microaneurysm, exudates, and bleeding of the image of the fundus. This study was performed to compare SVM method with KNN method for classification of diabetic retinopathy disease (mild, moderate, severe) based on exudate and microaneurysm image. For feature extraction uses wavelet method, and each of the two methods. This study made use of 160 test data, each of 40 images for normal class, mild class, moderate class, severe class. The accuracy obtained by KNN higher than SVM, with 65% and 62%. KNN classification method achieved the best results with the parameters K = 9, cityblock. While the classification with SVM method obtained the best results with parameters One agains all .Keywords: Diabetic Retinopathy, KNN, SVM, Wavelet.

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Classification of non-proliferative diabetic retinopathy based on hard exudates using soft margin SVM

Cited by 42 publications

References 9 publications

An automated grading system for diabetic retinopathy using curvelet transform and hierarchical classification

An automated grading system for diabetic retinopathy using curvelet transform and hierarchical classification

Classification of visualization exudates fundus images results using support vector machine

Analisis Perbandingan KNN dengan SVM untuk Klasifikasi Penyakit Diabetes Retinopati berdasarkan Citra Eksudat dan Mikroaneurisma

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