Applied and Computational Engineering
- The Open Access Proceedings Series for Conferences
Proceedings of the 4th International Conference on Signal Processing and Machine Learning
Series Vol. 49 , 22 March 2024
Open
Access | Article
Transfer learning approach for diabetic retinopathy detection using residual network
* Author to whom correspondence should be addressed.
Abstract
The condition known as diabetic retinopathy is a severe and common complication of diabetes. It affects the retina, which is a light-sensitive organ inside the eye, and it can lead to blindness or loss of vision. It is therefore important to improve the diagnosis and classification of this disorder. In deep learning, transfer learning is a process that aims to improve the performance of a given task by taking advantage of the knowledge that has been acquired from another. The main idea of this approach is to speed up the learning process by applying the obtained knowledge to a new task. In this paper, with the help of migration learning, a pre-trained deep learning model known as InceptionV3 was used to classify fundus images the Diabetic Retinopathy 2015 Data Colored Resized database in five categories according to the severity of the lesions. It was able to achieve a 92.314% accuracy on a test set.
Keywords
Diabetic retinopathy, InceptionV3, Transfer learning, Deep learning, Kappa consistency test
References
1. I. D. Federation. (2021) IDF Diabetes Atlas, 10th edn. Brussels, Belgium.
2. Ogurtsova K, Guariguata L, Barengo N C, et al. IDF diabetes Atlas: Global estimates of undiagnosed diabetes in adults for 2021[J]. Diabetes research and clinical practice, 2022, 183: 109118.
3. Diabetes complications. https://www.idf.org/aboutdiabetes/ complications.html.
4. M. Porta and J. Cunha-Vaz, Diabetes and the Eye. Cham: Springer International Publishing, 2019, pp. 1–43.
5. Sarhan A, Rokne J, Alhajj R, et al. Transfer learning through weighted loss function and group normalization for vessel segmentation from retinal images[C]//2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021: 9211-9218.
6. Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs[J]. JAMA, 2016, 316(22): 2402-2410.
7. Wu J H, Liu T Y A, Hsu W T, et al. Performance and limitation of machine learning algorithms for diabetic retinopathy screening: meta-analysis[J]. Journal of medical Internet research, 2021, 23(7): e23863.
8. Bhatia K, Arora S, Tomar R. Diagnosis of diabetic retinopathy using machine learning classification algorithm[C]//2016 2nd international conference on next generation computing technologies (NGCT). IEEE, 2016: 347-351.
9. Tsao H Y, Chan P Y, Su E C Y. Predicting diabetic retinopathy and identifying interpretable biomedical features using machine learning algorithms[J]. BMC bioinformatics, 2018, 19: 111-121.
10. Reddy G T, Bhattacharya S, Ramakrishnan S S, et al. An ensemble based machine learning model for diabetic retinopathy classification[C]//2020 international conference on emerging trends in information technology and engineering (ic-ETITE). IEEE, 2020: 1-6.
11. Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs[J]. JAMA, 2016, 316(22): 2402-2410.
12. Sarki R, Ahmed K, Wang H, et al. Automated detection of mild and multi-class diabetic eye diseases using deep learning[J]. Health Information Science and Systems, 2020, 8(1): 32.
13. Choi J Y, Yoo T K, Seo J G, et al. Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database[J]. PloS one, 2017, 12(11): e0187336.
14. Qummar S, Khan F G, Shah S, et al. A deep learning ensemble approach for diabetic retinopathy detection[J]. IEEE Access, 2019, 7: 150530-150539.
15. Kandel I, Castelli M. Transfer learning with convolutional neural networks for diabetic retinopathy image classification. A review[J]. Applied Sciences, 2020, 10(6): 2021.
16. Li X, Pang T, Xiong B, et al. Convolutional neural networks based transfer learning for diabetic retinopathy fundus image classification[C]//2017 10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI). IEEE, 2017: 1-11.
17. Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[J]. Advances in neural information processing systems, 2012, 25.
18. Mikołajczyk A, Grochowski M. Data augmentation for improving deep learning in image classification problem[C]//2018 international interdisciplinary PhD workshop (IIPhDW). IEEE, 2018: 117-122.
19. Xia X, Xu C, Nan B. Inception-v3 for flower classification[C]//2017 2nd international conference on image, vision and computing (ICIVC). IEEE, 2017: 783-787.
20. Bäck T, Fogel D B, Michalewicz Z. Handbook of evolutionary computation[J]. Release, 1997, 97(1): B1.
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open Access Instruction).
- Volume Title
- Proceedings of the 4th International Conference on Signal Processing and Machine Learning
- ISBN (Print)
- 978-1-83558-343-2
- ISBN (Online)
- 978-1-83558-344-9
- Published Date
- 22 March 2024
- Series
- Applied and Computational Engineering
- ISSN (Print)
- 2755-2721
- ISSN (Online)
- 2755-273X
- DOI
- 10.54254/2755-2721/49/20241698
- Copyright
- 22 March 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited