The Diagnosis Of Diabetic Retinopathy By Means Of Transfer Learning With Conventional Machine Learning Pipeline

Authors

  • Farhan Nabil Mohd Noor Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang
  • Wan Hasbullah Mohd Isa Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang
  • Anwar P.P. Abdul Majeed Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang

DOI:

https://doi.org/10.15282/mekatronika.v2i2.6769

Keywords:

Diabetic Retinopathy, Transfer Learning, SVM, kNN, RF

Abstract

Diabetic Retinopathy is one of the common eye diseases due to the complication of diabetes mellitus. Cotton wool spots, rough exudates, haemorrhages and microaneurysms are the symptoms of the diabetic retinopathy due to the fluid leakage that is caused by the high blood glucose level disorder. Early treatment to prevent a permanent blindness is important as it could save the diabetic retinopathy vision. Hence, in this study, we proposed to employ an automated detection method to diagnose the diabetic retinopathy. The dataset was obtained from the Kaggle Database and been divided for training, testing and validation purposes. Furthermore, Transfer Learning models, namely VGG19 were employed to extract the features before being processed by Machine Learning classifiers which are SVM, kNN and RF to classify the diabetic retinopathy. VGG19-SVM pipeline produced the best accuracy in training, testing and validation processes, achieving 99, 99 and 96 percents respectively.

Downloads

Published

2020-12-16

How to Cite

[1]
F. N. . Mohd Noor, W. H. . Mohd Isa, and A. P.P. Abdul Majeed, “The Diagnosis Of Diabetic Retinopathy By Means Of Transfer Learning With Conventional Machine Learning Pipeline”, MEKATRONIKA, vol. 2, no. 2, pp. 62–67, Dec. 2020.

Issue

Section

Original Article

Most read articles by the same author(s)