TELKOMNIKA Telecommunication Computing Electronics and Control
ResNet-n/DR: Automated diagnosis of diabetic retinopathy using a residual neural network
Dublin Core
Title
TELKOMNIKA Telecommunication Computing Electronics and Control
ResNet-n/DR: Automated diagnosis of diabetic retinopathy using a residual neural network
ResNet-n/DR: Automated diagnosis of diabetic retinopathy using a residual neural network
Subject
Convolutional neural networks
Deep learning
Diabetic retinopathy
Residual neural network
Deep learning
Diabetic retinopathy
Residual neural network
Description
Diabetic retinopathy (DR) is a progressive eye disease associated with
diabetes, resulting in blindness or blurred vision. The risk of vision loss was
dramatically decreased with early diagnosis and treatment. Doctors diagnose
DR by examining the fundus retinal images to develop lesions associated with
the disease. However, this diagnosis is a tedious and challenging task due to
growing undiagnosed and untreated DR cases and the variability of retinal
changes across disease stages. Manually analyzing the images has become an
expensive and time-consuming task, not to mention that training new
specialists takes time and requires daily practice. Our work investigates deep
learning methods, particularly convolutional neural network (CNN), for DR
diagnosis in the disease’s five stages. A pre-trained residual neural network
(ResNet-34) was trained and tested for DR. Then, we develop computationally
efficient and scalable methods after modifying a ResNet-34 with three additional
residual units as a novel ResNet-n/DR. The Asia Pacific Tele-Ophthalmology
Society (APTOS) 2019 dataset was used to evaluate the performance of
models after applying multiple pre-processing steps to eliminate image noise
and improve color contrast, thereby increasing efficiency. Our findings
achieved state-of-the-art results compared to previous studies that used the
same dataset. It had 90.7% sensitivity, 93.5% accuracy, 98.2% specificity,
89.5% precision, and 90.1% F1 score.
diabetes, resulting in blindness or blurred vision. The risk of vision loss was
dramatically decreased with early diagnosis and treatment. Doctors diagnose
DR by examining the fundus retinal images to develop lesions associated with
the disease. However, this diagnosis is a tedious and challenging task due to
growing undiagnosed and untreated DR cases and the variability of retinal
changes across disease stages. Manually analyzing the images has become an
expensive and time-consuming task, not to mention that training new
specialists takes time and requires daily practice. Our work investigates deep
learning methods, particularly convolutional neural network (CNN), for DR
diagnosis in the disease’s five stages. A pre-trained residual neural network
(ResNet-34) was trained and tested for DR. Then, we develop computationally
efficient and scalable methods after modifying a ResNet-34 with three additional
residual units as a novel ResNet-n/DR. The Asia Pacific Tele-Ophthalmology
Society (APTOS) 2019 dataset was used to evaluate the performance of
models after applying multiple pre-processing steps to eliminate image noise
and improve color contrast, thereby increasing efficiency. Our findings
achieved state-of-the-art results compared to previous studies that used the
same dataset. It had 90.7% sensitivity, 93.5% accuracy, 98.2% specificity,
89.5% precision, and 90.1% F1 score.
Creator
Noor M. Al-Moosawi, Raidah S. Khudeyer
Source
http://telkomnika.uad.ac.id
Date
Feb 16, 2023
Contributor
peri irawan
Format
pdf
Language
english
Type
text
Files
Collection
Citation
Noor M. Al-Moosawi, Raidah S. Khudeyer, “TELKOMNIKA Telecommunication Computing Electronics and Control
ResNet-n/DR: Automated diagnosis of diabetic retinopathy using a residual neural network,” Repository Horizon University Indonesia, accessed November 14, 2024, https://repository.horizon.ac.id/items/show/4598.
ResNet-n/DR: Automated diagnosis of diabetic retinopathy using a residual neural network,” Repository Horizon University Indonesia, accessed November 14, 2024, https://repository.horizon.ac.id/items/show/4598.