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- Vol. 5 Issue 6
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Classification of Diabetes Retinopathy Using Deep Learning
Authors
1234 SKN Sinhgad Institute of Technology & Science Lonavala, Pune, Maharashtra, India. 5Asst. Professor, SKN Sinhgad Institute of Technology & Science Lonavala, Pune, Maharashtra, India.
Article Information
- DOI
- No DOI
- Published In
- Volume 5, Issue 6 (2024)
- Published Online
- November-December 2024
- Pages
- 20-23
- Article Type
- Research Article
- Check for updates
- No DOI available for cross-reference
References
1. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.URL:[https://www.nature.com/articles/nature14539] (https://www.nature.com/articles/nature14539) - A comprehensive overview of deep
learning, including foundational concepts and algorithms.
2. Gulshan, V., Peng, L., Coram, M., Stumpe, M., Wu, D., & Patel, S. N., et al. (2016). Development and validation of a deep learning
algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316(22), 2402-2410.
- DOI: [10.1001/jama.2016.17216] (https://doi.org/10.1001/jama.2016.17216)
- Describes a deep learning model for detecting diabetic retinopathy from retinal images.
3. Rajalakshmi, R., C. R. L. K., & P. M. S. (2018). Automated diabetic retinopathy detection in smartphone-based fundus photography
using deep learning. Eye, 32(6), 1138-1144.
- DOI: [10.1038/s41433-018-0086-7] (https://doi.org/10.1038/s41433-018-0086-7)
- Discusses the use of deep learning for automated DR detection using smartphone-based.
4. Dai, L., Ding, X., & Zhou, F. (2021). Deep learning-based methods for diabetic retinopathy detection: A review. IEEE Access, 9,
11486-11500.
- DOI: [10.1109/ACCESS.2021.3058811] (https://doi.org/10.1109/ACCESS.2021.3058811)
- A review of various deep learning methods applied to diabetic retinopathy detection.
5. Siddique, M., Wang, Y., & Wang, X. (2020). A survey on deep learning for diabetic retinopathy diagnosis. Journal of Healthcare
Engineering, 2020, Article ID 7579132.
- DOI: [10.1155/2020/7579132] (https://doi.org/10.1155/2020/7579132)
- Provides an overview of deep learning techniques specifically for DR diagnosis.
6. Wang, Y., Li, Y., & Chen, Z. (2021). Performance evaluation of deep learning algorithms for detecting diabetic retinopathy from
fundus images. Medical Image Analysis, 71, 102043.
- DOI: [10.1016/j.media.2021.102043] (https://doi.org/10.1016/j.media.2021.102043)
- Evaluates the performance of different deep learning algorithms for DR detection.
7. Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., & Baxter, S. L., et al. (2018). Identifying medical diagnoses
and treatable diseases by image-based deep learning. Cell, 172(5), 1122-1131.e9.
- DOI: [10.1016/j.cell.2018.02.010] (https://doi.org/10.1016/j.cell.2018.02.010)
- Discusses the application of deep learning to medical image analysis, including DR.
learning, including foundational concepts and algorithms.
2. Gulshan, V., Peng, L., Coram, M., Stumpe, M., Wu, D., & Patel, S. N., et al. (2016). Development and validation of a deep learning
algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316(22), 2402-2410.
- DOI: [10.1001/jama.2016.17216] (https://doi.org/10.1001/jama.2016.17216)
- Describes a deep learning model for detecting diabetic retinopathy from retinal images.
3. Rajalakshmi, R., C. R. L. K., & P. M. S. (2018). Automated diabetic retinopathy detection in smartphone-based fundus photography
using deep learning. Eye, 32(6), 1138-1144.
- DOI: [10.1038/s41433-018-0086-7] (https://doi.org/10.1038/s41433-018-0086-7)
- Discusses the use of deep learning for automated DR detection using smartphone-based.
4. Dai, L., Ding, X., & Zhou, F. (2021). Deep learning-based methods for diabetic retinopathy detection: A review. IEEE Access, 9,
11486-11500.
- DOI: [10.1109/ACCESS.2021.3058811] (https://doi.org/10.1109/ACCESS.2021.3058811)
- A review of various deep learning methods applied to diabetic retinopathy detection.
5. Siddique, M., Wang, Y., & Wang, X. (2020). A survey on deep learning for diabetic retinopathy diagnosis. Journal of Healthcare
Engineering, 2020, Article ID 7579132.
- DOI: [10.1155/2020/7579132] (https://doi.org/10.1155/2020/7579132)
- Provides an overview of deep learning techniques specifically for DR diagnosis.
6. Wang, Y., Li, Y., & Chen, Z. (2021). Performance evaluation of deep learning algorithms for detecting diabetic retinopathy from
fundus images. Medical Image Analysis, 71, 102043.
- DOI: [10.1016/j.media.2021.102043] (https://doi.org/10.1016/j.media.2021.102043)
- Evaluates the performance of different deep learning algorithms for DR detection.
7. Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., & Baxter, S. L., et al. (2018). Identifying medical diagnoses
and treatable diseases by image-based deep learning. Cell, 172(5), 1122-1131.e9.
- DOI: [10.1016/j.cell.2018.02.010] (https://doi.org/10.1016/j.cell.2018.02.010)
- Discusses the application of deep learning to medical image analysis, including DR.
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