TY - GEN
T1 - The Diagnosis of Diabetic Retinopathy
T2 - 21st International Conference on Control, Automation and Systems, ICCAS 2021
AU - Noor, Farhan Nabil Mohd
AU - Majeed, Anwar P.P.Abdul
AU - Razmam, Mohd Azraai Mod
AU - Khairuddin, Ismail Mohd
AU - Isa, Wan Hasbullah Mohd
N1 - Publisher Copyright:
© 2021 ICROS.
PY - 2021
Y1 - 2021
N2 - Diabetic Retinopathy is one of the complications of diabetes mellitus that occurs to the eye. It damages the blood vessels, which cause the leaking of the blood and other fluids due to the elevated blood glucose level. Diabetic Retinopathy is a quiet ailment that patients may not discover until abnormalities in the retina have progressed to the point that medication is difficult or impossible. It can also result in patients losing their sight completely. However, an automated screening machine may help overcome this problem by helping the ophthalmologist diagnose diabetic retinopathy patients as soon as possible. Hence, this research investigates the effectiveness of automatic screening machine by employing the Transfer Learning model such as VGG16 to extract the features and fed them to the Support Vector Machine (SVM), k-Nearest Neighbour (kNN) and Random Forest (RF) for the classification. It was shown that the VGG16-SVM pipeline displayed the most promising performance on the classification of Diabetic Retinopathy.
AB - Diabetic Retinopathy is one of the complications of diabetes mellitus that occurs to the eye. It damages the blood vessels, which cause the leaking of the blood and other fluids due to the elevated blood glucose level. Diabetic Retinopathy is a quiet ailment that patients may not discover until abnormalities in the retina have progressed to the point that medication is difficult or impossible. It can also result in patients losing their sight completely. However, an automated screening machine may help overcome this problem by helping the ophthalmologist diagnose diabetic retinopathy patients as soon as possible. Hence, this research investigates the effectiveness of automatic screening machine by employing the Transfer Learning model such as VGG16 to extract the features and fed them to the Support Vector Machine (SVM), k-Nearest Neighbour (kNN) and Random Forest (RF) for the classification. It was shown that the VGG16-SVM pipeline displayed the most promising performance on the classification of Diabetic Retinopathy.
KW - Diabetic Retinopathy
KW - kNN
KW - RF
KW - SVM
KW - Transfer Learning
KW - VGG16
UR - http://www.scopus.com/inward/record.url?scp=85124229464&partnerID=8YFLogxK
U2 - 10.23919/ICCAS52745.2021.9649801
DO - 10.23919/ICCAS52745.2021.9649801
M3 - Conference Proceeding
AN - SCOPUS:85124229464
T3 - International Conference on Control, Automation and Systems
SP - 596
EP - 601
BT - 2021 21st International Conference on Control, Automation and Systems, ICCAS 2021
PB - IEEE Computer Society
Y2 - 12 October 2021 through 15 October 2021
ER -
