Assessment of transfer learning models for grading of diabetic retinopathy
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2023.14.2.17Keywords:
Transfer learning, retinal image, diabetic retinopathy, VGG16, Inception v3, ResNet50Dimensions Badge
Issue
Section
License
Copyright (c) 2023 The Scientific Temper
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Diabetic retinopathy is a potentially mortal diabetic complication. The severity level of DR must be identified earlier to reduce the medical complications. Effective automated ways for identifying DR and classifying its severity stage are necessary to reduce the burden on ophthalmologists. Transfer learning methods are utilized to automatically grade the severity of diabetic retinopathy in this study. The stages of DR are diagnosed using pretrained VGG16, Inception v3, and ResNet50 models on pre-processed retinal images of DDR dataset. Out of three implemented models, Inception v3 achieved higher validation accuracy of 76.47% and testing accuracy of 67% compared to VGG16 and ResNet50 models. This research contributes to the analysis of deep learning architectures for the creation of automated diabetic retinopathy stage diagnosis and gradingAbstract
How to Cite
Downloads
Similar Articles
- Temesgen A. Asfaw, Batch size impact on enset leaf disease detection , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- Isaac Asampana, Henry M. Akwetey, Ben Ocra, Jones Y. Nyame, Albert A. Akanferi, Hannah A. Tanye, Factors motivating the adoption of virtual learning environments in higher education. Is gender relevant? , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- R. Kalaiselvi, P. Meenakshi Sundaram, Unified framework for sybil attack detection in mobile ad hoc networks using machine learning approach , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- M. Rajalakshmi, V. Sulochana, Enhancing deep learning model performance in air quality classification through probabilistic hyperparameter tuning with tree-structured Parzen estimators , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Ganga Gudi, Mallamma V Reddy, Hanumanthappa M, Enhancing Kannada text-to-speech and braille conversion with deep learning for the visually impaired , The Scientific Temper: Vol. 16 No. Spl-1 (2025): The Scientific Temper
- Medha, Improvising the Mind: Metacognitive Skill Formation Through Musical Practice Among Youth , The Scientific Temper: Vol. 17 No. 01 (2026): The Scientific Temper
- Adedotun Adedayo F, Odusanya Oluwaseun A, Adesina Olumide S, Adeyiga J. A, Okagbue, Hilary I, Oyewole O, Prediction of automobile insurance fraud claims using machine learning , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- P. S. Dheepika, V. Umadevi, An optimized approach for detection and mitigation of DDoS attack cloud using an ensembled deep learning approach , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Pritee Rajaram Ray, Bijal Zaveri, Inclusive education for children with learning difficulties in Mauritius: An analytical study among select stakeholders , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Merlin Sofia S, D. Ravindran, G. Arockia Sahaya Sheela, Clean Balance-Ensemble CHD: A Balanced Ensemble Learning Framework for Accurate Coronary Heart Disease Prediction , The Scientific Temper: Vol. 16 No. 10 (2025): The Scientific Temper
<< < 2 3 4 5 6 7 8 9 10 11 > >>
You may also start an advanced similarity search for this article.
Most read articles by the same author(s)
- Sowmiya M, Banu Rekha B, Malar E, Ensemble classifiers with hybrid feature selection approach for diagnosis of coronary artery disease , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
