TY - GEN
T1 - Detection of hypertensive retinopathy using principal component analysis (PCA) and backpropagation neural network methods
AU - Arasy, Rahmat
AU - Basari,
N1 - Publisher Copyright:
© 2019 Author(s).
PY - 2019/4/9
Y1 - 2019/4/9
N2 - Hypertensive Retinopathy (HTR) is a disease caused by high blood pressure flowing into the retinal blood vessels, resulting in thickening of blood vessel walls and reducing blood flow in the retina. Complications arising from this disease are diverse and dangerous, ranging from retinal vein occlusion, eye nerve damage, even blindness. This paper proposed a system for hypertensive retinopathy detection by using Principal Component Analysis (PCA) dan Backpropagation Neural Network (BNN). Retinal image was taken from STARE database which separated into learning and testing data with a ratio 7 to 3. PCA as fundus-image-dimension-reduction method has successfully reduced fundus image raw data by 99.9% reduction, thus cutting computation load for neural network training. This paper presented Backpropagation Neural Network (BNN) as main classification algorithm which done by setting its parameters, learning and testing the data. So the model could classify retinal image into one of two classes, namely the normal retina and retina with high blood pressure, based on the BNN output result. The proposed model result showed that testing accuracy up to 86.36%.
AB - Hypertensive Retinopathy (HTR) is a disease caused by high blood pressure flowing into the retinal blood vessels, resulting in thickening of blood vessel walls and reducing blood flow in the retina. Complications arising from this disease are diverse and dangerous, ranging from retinal vein occlusion, eye nerve damage, even blindness. This paper proposed a system for hypertensive retinopathy detection by using Principal Component Analysis (PCA) dan Backpropagation Neural Network (BNN). Retinal image was taken from STARE database which separated into learning and testing data with a ratio 7 to 3. PCA as fundus-image-dimension-reduction method has successfully reduced fundus image raw data by 99.9% reduction, thus cutting computation load for neural network training. This paper presented Backpropagation Neural Network (BNN) as main classification algorithm which done by setting its parameters, learning and testing the data. So the model could classify retinal image into one of two classes, namely the normal retina and retina with high blood pressure, based on the BNN output result. The proposed model result showed that testing accuracy up to 86.36%.
KW - Backpropagation Neural Network
KW - Hypertensive Retinopathy
KW - Principal Component Analysis
KW - Retina
UR - http://www.scopus.com/inward/record.url?scp=85064823677&partnerID=8YFLogxK
U2 - 10.1063/1.5096735
DO - 10.1063/1.5096735
M3 - Conference contribution
AN - SCOPUS:85064823677
T3 - AIP Conference Proceedings
BT - 3rd Biomedical Engineering''s Recent Progress in Biomaterials, Drugs Development, and Medical Devices
A2 - Wulan, Praswasti P.D.K.
A2 - Gozan, Misri
A2 - Astutiningsih, Sotya
A2 - Ramahdita, Ghiska
A2 - Dhelika, Radon
A2 - Kreshanti, Prasetyanugraheni
PB - American Institute of Physics Inc.
T2 - 3rd International Symposium of Biomedical Engineering''s Recent Progress in Biomaterials, Drugs Development, and Medical Devices, ISBE 2018
Y2 - 6 August 2018 through 8 August 2018
ER -