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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