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Detecting Lesion Characteristics of Diabetic Retinopathy Using Machine Learning and Computer Vision

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@article{IJASEIT8876,
   author = {Alhadi Bustamam and Devvi Sarwinda and Bariqi Abdillah and Tesdiq Prigel Kaloka},
   title = {Detecting Lesion Characteristics of Diabetic Retinopathy Using Machine Learning and Computer Vision},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {10},
   number = {4},
   year = {2020},
   pages = {1367--1373},
   keywords = {diabetic retinopathy; LBP; GLCM; SFTA; KNN; SVM},
   abstract = {One indicator of the severity of diabetic retinopathy is the existence of lesion characteristics in the eyes such as microaneurysm, hemorrhages, exudates, and neovascularization. Without proper early medical attention, this lesion could lead to blindness. Considering its importance, a system that could detect such lesions will be beneficial. This paper investigates the lesion characteristics of diabetic retinopathy from fundus images such as microaneurysm (redsmalldots), exudates, hemorrhages, and neovascularization. In this study, we present three of feature extraction methods, i.e., Local Binary Pattern (LBP), Gray Level Co-Occurrence Matrix (GLCM) and Segmentation Fractal Texture Analysis (SFTA). K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) chosen as classifiers for classifying five classes (redsmalldots, hemorrhages, hard exudates, soft exudates, and neovascularization). The data used in this research obtained from DiaretDB0 database. The experimental results show that our proposed method can detect the lesion characteristics of diabetic retinopathy with higher accuracy of 86,84% and 96% for SVM and KNN, respectively.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8876},
   doi = {10.18517/ijaseit.10.4.8876}
}

EndNote

%A Bustamam, Alhadi
%A Sarwinda, Devvi
%A Abdillah, Bariqi
%A Kaloka, Tesdiq Prigel
%D 2020
%T Detecting Lesion Characteristics of Diabetic Retinopathy Using Machine Learning and Computer Vision
%B 2020
%9 diabetic retinopathy; LBP; GLCM; SFTA; KNN; SVM
%! Detecting Lesion Characteristics of Diabetic Retinopathy Using Machine Learning and Computer Vision
%K diabetic retinopathy; LBP; GLCM; SFTA; KNN; SVM
%X One indicator of the severity of diabetic retinopathy is the existence of lesion characteristics in the eyes such as microaneurysm, hemorrhages, exudates, and neovascularization. Without proper early medical attention, this lesion could lead to blindness. Considering its importance, a system that could detect such lesions will be beneficial. This paper investigates the lesion characteristics of diabetic retinopathy from fundus images such as microaneurysm (redsmalldots), exudates, hemorrhages, and neovascularization. In this study, we present three of feature extraction methods, i.e., Local Binary Pattern (LBP), Gray Level Co-Occurrence Matrix (GLCM) and Segmentation Fractal Texture Analysis (SFTA). K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) chosen as classifiers for classifying five classes (redsmalldots, hemorrhages, hard exudates, soft exudates, and neovascularization). The data used in this research obtained from DiaretDB0 database. The experimental results show that our proposed method can detect the lesion characteristics of diabetic retinopathy with higher accuracy of 86,84% and 96% for SVM and KNN, respectively.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8876
%R doi:10.18517/ijaseit.10.4.8876
%J International Journal on Advanced Science, Engineering and Information Technology
%V 10
%N 4
%@ 2088-5334

IEEE

Alhadi Bustamam,Devvi Sarwinda,Bariqi Abdillah and Tesdiq Prigel Kaloka,"Detecting Lesion Characteristics of Diabetic Retinopathy Using Machine Learning and Computer Vision," International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 4, pp. 1367-1373, 2020. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.10.4.8876.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Bustamam, Alhadi
AU  - Sarwinda, Devvi
AU  - Abdillah, Bariqi
AU  - Kaloka, Tesdiq Prigel
PY  - 2020
TI  - Detecting Lesion Characteristics of Diabetic Retinopathy Using Machine Learning and Computer Vision
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 10 (2020) No. 4
Y2  - 2020
SP  - 1367
EP  - 1373
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - diabetic retinopathy; LBP; GLCM; SFTA; KNN; SVM
N2  - One indicator of the severity of diabetic retinopathy is the existence of lesion characteristics in the eyes such as microaneurysm, hemorrhages, exudates, and neovascularization. Without proper early medical attention, this lesion could lead to blindness. Considering its importance, a system that could detect such lesions will be beneficial. This paper investigates the lesion characteristics of diabetic retinopathy from fundus images such as microaneurysm (redsmalldots), exudates, hemorrhages, and neovascularization. In this study, we present three of feature extraction methods, i.e., Local Binary Pattern (LBP), Gray Level Co-Occurrence Matrix (GLCM) and Segmentation Fractal Texture Analysis (SFTA). K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) chosen as classifiers for classifying five classes (redsmalldots, hemorrhages, hard exudates, soft exudates, and neovascularization). The data used in this research obtained from DiaretDB0 database. The experimental results show that our proposed method can detect the lesion characteristics of diabetic retinopathy with higher accuracy of 86,84% and 96% for SVM and KNN, respectively.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8876
DO  - 10.18517/ijaseit.10.4.8876

RefWorks

RT Journal Article
ID 8876
A1 Bustamam, Alhadi
A1 Sarwinda, Devvi
A1 Abdillah, Bariqi
A1 Kaloka, Tesdiq Prigel
T1 Detecting Lesion Characteristics of Diabetic Retinopathy Using Machine Learning and Computer Vision
JF International Journal on Advanced Science, Engineering and Information Technology
VO 10
IS 4
YR 2020
SP 1367
OP 1373
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 diabetic retinopathy; LBP; GLCM; SFTA; KNN; SVM
AB One indicator of the severity of diabetic retinopathy is the existence of lesion characteristics in the eyes such as microaneurysm, hemorrhages, exudates, and neovascularization. Without proper early medical attention, this lesion could lead to blindness. Considering its importance, a system that could detect such lesions will be beneficial. This paper investigates the lesion characteristics of diabetic retinopathy from fundus images such as microaneurysm (redsmalldots), exudates, hemorrhages, and neovascularization. In this study, we present three of feature extraction methods, i.e., Local Binary Pattern (LBP), Gray Level Co-Occurrence Matrix (GLCM) and Segmentation Fractal Texture Analysis (SFTA). K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) chosen as classifiers for classifying five classes (redsmalldots, hemorrhages, hard exudates, soft exudates, and neovascularization). The data used in this research obtained from DiaretDB0 database. The experimental results show that our proposed method can detect the lesion characteristics of diabetic retinopathy with higher accuracy of 86,84% and 96% for SVM and KNN, respectively.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8876
DO  - 10.18517/ijaseit.10.4.8876