Development of mobile application for detection and grading of diabetic retinopathy

The key to preventing blindness caused by diabetic retinopathy (DR) is regular screening and early recognition during its early stages. Currently, DR grading is done manually by ophthalmologists and trained graders where the process is time-consuming. Therefore, this paper aims to develop a mobile a...

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Main Authors: Kipli, Kuryati, Lee, Yee Hui, Tajudin, Nurul Mirza Afiqah, Sapawi, Rohana, Sahari, Siti Kudnie, Awang Mat, Dayang Azra, A. Jalil, M., Ray, Kanad, Kaiser, M. Shamim, Mahmud, Mufti
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Published: Springer Science and Business Media Deutschland GmbH 2022
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Online Access:http://eprints.utm.my/id/eprint/101088/
http://dx.doi.org/10.1007/978-981-16-8826-3_29
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spelling my.utm.1010882023-05-27T07:45:23Z http://eprints.utm.my/id/eprint/101088/ Development of mobile application for detection and grading of diabetic retinopathy Kipli, Kuryati Lee, Yee Hui Tajudin, Nurul Mirza Afiqah Sapawi, Rohana Sahari, Siti Kudnie Awang Mat, Dayang Azra A. Jalil, M. Ray, Kanad Kaiser, M. Shamim Mahmud, Mufti QA75 Electronic computers. Computer science The key to preventing blindness caused by diabetic retinopathy (DR) is regular screening and early recognition during its early stages. Currently, DR grading is done manually by ophthalmologists and trained graders where the process is time-consuming. Therefore, this paper aims to develop a mobile app that can provide DR detection and grading without a professional or doctor. The patients will be referred to ophthalmologists if further evaluations are required. This research builds an image classification within a mobile application by using deep learning techniques which utilized the Google AI technologies: Google TensorFlow and Google Cloud Platform (Cloud AutoML and Cloud storage). Image classification is performed in two layers which involve DR detection and grading. A total of 12,062 fundus images are chosen from the dataset collected and undergo image preprocessing. The preprocessed images are used to train the model in TensorFlow and Cloud AutoML, respectively. The model will be implemented into the mobile application after being trained with high accuracy. The final test accuracy for the MobileNet pretrained model is 82.9%, while averaging precision for the model of Cloud AutoML is 75%. Further research is required to improve the stability of this algorithm and mobile app for real clinical environment settings. Springer Science and Business Media Deutschland GmbH 2022 Book Section PeerReviewed Kipli, Kuryati and Lee, Yee Hui and Tajudin, Nurul Mirza Afiqah and Sapawi, Rohana and Sahari, Siti Kudnie and Awang Mat, Dayang Azra and A. Jalil, M. and Ray, Kanad and Kaiser, M. Shamim and Mahmud, Mufti (2022) Development of mobile application for detection and grading of diabetic retinopathy. In: Proceedings of Trends in Electronics and Health Informatics TEHI 2021. Lecture Notes in Networks and Systems, 376 (NA). Springer Science and Business Media Deutschland GmbH, Singapore, pp. 339-349. ISBN 978-981168825-6 http://dx.doi.org/10.1007/978-981-16-8826-3_29 DOI:10.1007/978-981-16-8826-3_29
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Kipli, Kuryati
Lee, Yee Hui
Tajudin, Nurul Mirza Afiqah
Sapawi, Rohana
Sahari, Siti Kudnie
Awang Mat, Dayang Azra
A. Jalil, M.
Ray, Kanad
Kaiser, M. Shamim
Mahmud, Mufti
Development of mobile application for detection and grading of diabetic retinopathy
description The key to preventing blindness caused by diabetic retinopathy (DR) is regular screening and early recognition during its early stages. Currently, DR grading is done manually by ophthalmologists and trained graders where the process is time-consuming. Therefore, this paper aims to develop a mobile app that can provide DR detection and grading without a professional or doctor. The patients will be referred to ophthalmologists if further evaluations are required. This research builds an image classification within a mobile application by using deep learning techniques which utilized the Google AI technologies: Google TensorFlow and Google Cloud Platform (Cloud AutoML and Cloud storage). Image classification is performed in two layers which involve DR detection and grading. A total of 12,062 fundus images are chosen from the dataset collected and undergo image preprocessing. The preprocessed images are used to train the model in TensorFlow and Cloud AutoML, respectively. The model will be implemented into the mobile application after being trained with high accuracy. The final test accuracy for the MobileNet pretrained model is 82.9%, while averaging precision for the model of Cloud AutoML is 75%. Further research is required to improve the stability of this algorithm and mobile app for real clinical environment settings.
format Book Section
author Kipli, Kuryati
Lee, Yee Hui
Tajudin, Nurul Mirza Afiqah
Sapawi, Rohana
Sahari, Siti Kudnie
Awang Mat, Dayang Azra
A. Jalil, M.
Ray, Kanad
Kaiser, M. Shamim
Mahmud, Mufti
author_facet Kipli, Kuryati
Lee, Yee Hui
Tajudin, Nurul Mirza Afiqah
Sapawi, Rohana
Sahari, Siti Kudnie
Awang Mat, Dayang Azra
A. Jalil, M.
Ray, Kanad
Kaiser, M. Shamim
Mahmud, Mufti
author_sort Kipli, Kuryati
title Development of mobile application for detection and grading of diabetic retinopathy
title_short Development of mobile application for detection and grading of diabetic retinopathy
title_full Development of mobile application for detection and grading of diabetic retinopathy
title_fullStr Development of mobile application for detection and grading of diabetic retinopathy
title_full_unstemmed Development of mobile application for detection and grading of diabetic retinopathy
title_sort development of mobile application for detection and grading of diabetic retinopathy
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2022
url http://eprints.utm.my/id/eprint/101088/
http://dx.doi.org/10.1007/978-981-16-8826-3_29
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score 13.209306