Universiti Teknologi Malaysia Institutional Repository

Development of mobile application for detection and grading of diabetic retinopathy

Kipli, Kuryati and Lee, Yee Hui and Tajudin, Nurul Mirza Afiqah and Sapawi, Rohana and Sahari, Siti Kudnie and Awang Mat, Dayang Azra and Jalil, M. A. and Ray, Kanad and Kaiser, M. Shamim and Mahmud, Mufti (2022) Development of mobile application for detection and grading of diabetic retinopathy. In: 1st International Conference on Trends in Electronics and Health Informatics, TEHI 2021, 16 December 2021 - 17 December 2021, Virtual, Online.

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Official URL: http://dx.doi.org/10.1007/978-981-16-8826-3_29


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.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:cloud AutoML, diabetic retinopathy, google cloud platform, google tensorflow, image classification, mobile application
Subjects:Q Science > QC Physics
ID Code:98737
Deposited By: Yanti Mohd Shah
Deposited On:02 Feb 2023 16:06
Last Modified:02 Feb 2023 16:06

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