TY - GEN
T1 - The Diagnosis of Diabetic Retinopathy
T2 - 9th International Conference on Robot Intelligence Technology and Applications, RiTA 2021
AU - Mohd Noor, Farhan Nabil
AU - Mohd Isa, Wan Hasbullah
AU - Khairuddin, Ismail Mohd
AU - Mohd Razman, Mohd Azraai
AU - Musa, Rabiu Muazu
AU - Ahmad, Ahmad Fakhri
AU - P. P. Abdul Majeed, Anwar
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Diabetic Retinopathy (DR) is a type of eye disease that is caused by diabetes mellitus. The elevated blood glucose level causes the expansion of the blood vessels that results in the leaking of the blood and other fluids. DR is a silent disease in which those inflicted with it are unaware until irregularities in the retina have advanced to the point where treatment is difficult or impossible to administer, resulting in them losing their sight completely. However, it is worth noting that early treatment can solve this problem. Hence, the purpose of this study is to develop a transfer learning pipeline for diagnosing DR. The data in the present study was obtained from the Kaggle database, and the pre-trained InceptionV3 model was employed to extract the features from the images acquired. The features are fed into the three different classifiers, namely, Support Vector Machine (SVM), k-Nearest Neighbour (kNN) and the Random Forest (RF). It was shown from the present investigation that the InceptionV3-SVM pipeline demonstrated the best performance by achieving 100%, 98% and 96% classification accuracy for the training, testing and validation dataset. The results further suggest the possible deployment of the pipeline for the diagnosis of DR.
AB - Diabetic Retinopathy (DR) is a type of eye disease that is caused by diabetes mellitus. The elevated blood glucose level causes the expansion of the blood vessels that results in the leaking of the blood and other fluids. DR is a silent disease in which those inflicted with it are unaware until irregularities in the retina have advanced to the point where treatment is difficult or impossible to administer, resulting in them losing their sight completely. However, it is worth noting that early treatment can solve this problem. Hence, the purpose of this study is to develop a transfer learning pipeline for diagnosing DR. The data in the present study was obtained from the Kaggle database, and the pre-trained InceptionV3 model was employed to extract the features from the images acquired. The features are fed into the three different classifiers, namely, Support Vector Machine (SVM), k-Nearest Neighbour (kNN) and the Random Forest (RF). It was shown from the present investigation that the InceptionV3-SVM pipeline demonstrated the best performance by achieving 100%, 98% and 96% classification accuracy for the training, testing and validation dataset. The results further suggest the possible deployment of the pipeline for the diagnosis of DR.
KW - Diabetic retinopathy
KW - InceptionV3
KW - RF
KW - SVM
KW - Transfer learning
KW - kNN
UR - https://www.scopus.com/pages/publications/85128481872
U2 - 10.1007/978-3-030-97672-9_35
DO - 10.1007/978-3-030-97672-9_35
M3 - Conference Proceeding
SN - 9783030976712
VL - 429 LNNS
T3 - Lecture Notes in Networks and Systems
SP - 392
EP - 397
BT - Robot Intelligence Technology and Applications 6 - Results from the 9th International Conference on Robot Intelligence Technology and Applications
A2 - Kim, Jinwhan
A2 - Englot, Brendan
A2 - Park, Hae-Won
A2 - Choi, Han-Lim
A2 - Myung, Hyun
A2 - Kim, Junmo
A2 - Kim, Jong-Hwan
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 16 December 2021 through 17 December 2021
ER -
