The diagnosis of diabetic retinopathy by means of transfer learning with conventional machine learning pipeline
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...
Saved in:
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Penerbit UMP
2020
|
Subjects: | |
Online Access: | http://umpir.ump.edu.my/id/eprint/33640/1/The%20diagnosis%20of%20diabetic%20retinopathy%20by%20means%20of%20transfer%20learning.pdf http://umpir.ump.edu.my/id/eprint/33640/ https://doi.org/10.15282/mekatronika.v2i2.6769 https://doi.org/10.15282/mekatronika.v2i2.6769 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Universiti Malaysia Pahang |
Language: | English |
id |
my.ump.umpir.33640 |
---|---|
record_format |
eprints |
spelling |
my.ump.umpir.336402022-04-07T02:04:49Z http://umpir.ump.edu.my/id/eprint/33640/ The diagnosis of diabetic retinopathy by means of transfer learning with conventional machine learning pipeline Farhan Nabil, Mohd Noor Wan Hasbullah, Mohd Isa Anwar P. P., Abdul Majeed TJ Mechanical engineering and machinery TS Manufactures 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. Penerbit UMP 2020 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/33640/1/The%20diagnosis%20of%20diabetic%20retinopathy%20by%20means%20of%20transfer%20learning.pdf Farhan Nabil, Mohd Noor and Wan Hasbullah, Mohd Isa and Anwar P. P., Abdul Majeed (2020) The diagnosis of diabetic retinopathy by means of transfer learning with conventional machine learning pipeline. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 2 (2). pp. 62-67. ISSN 2637-0883 https://doi.org/10.15282/mekatronika.v2i2.6769 https://doi.org/10.15282/mekatronika.v2i2.6769 |
institution |
Universiti Malaysia Pahang |
building |
UMP Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Malaysia Pahang |
content_source |
UMP Institutional Repository |
url_provider |
http://umpir.ump.edu.my/ |
language |
English |
topic |
TJ Mechanical engineering and machinery TS Manufactures |
spellingShingle |
TJ Mechanical engineering and machinery TS Manufactures Farhan Nabil, Mohd Noor Wan Hasbullah, Mohd Isa Anwar P. P., Abdul Majeed The diagnosis of diabetic retinopathy by means of transfer learning with conventional machine learning pipeline |
description |
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. |
format |
Article |
author |
Farhan Nabil, Mohd Noor Wan Hasbullah, Mohd Isa Anwar P. P., Abdul Majeed |
author_facet |
Farhan Nabil, Mohd Noor Wan Hasbullah, Mohd Isa Anwar P. P., Abdul Majeed |
author_sort |
Farhan Nabil, Mohd Noor |
title |
The diagnosis of diabetic retinopathy by means of transfer learning with conventional machine learning pipeline |
title_short |
The diagnosis of diabetic retinopathy by means of transfer learning with conventional machine learning pipeline |
title_full |
The diagnosis of diabetic retinopathy by means of transfer learning with conventional machine learning pipeline |
title_fullStr |
The diagnosis of diabetic retinopathy by means of transfer learning with conventional machine learning pipeline |
title_full_unstemmed |
The diagnosis of diabetic retinopathy by means of transfer learning with conventional machine learning pipeline |
title_sort |
diagnosis of diabetic retinopathy by means of transfer learning with conventional machine learning pipeline |
publisher |
Penerbit UMP |
publishDate |
2020 |
url |
http://umpir.ump.edu.my/id/eprint/33640/1/The%20diagnosis%20of%20diabetic%20retinopathy%20by%20means%20of%20transfer%20learning.pdf http://umpir.ump.edu.my/id/eprint/33640/ https://doi.org/10.15282/mekatronika.v2i2.6769 https://doi.org/10.15282/mekatronika.v2i2.6769 |
_version_ |
1729703440884432896 |