ARCHIVES
Research Article
Classification of Diabetes Retinopathy Using Deep Learning
Rohan Pawashe1
Vinayak Pise2
Chetan Patil3
Vaibhav Kadam4
Shankar Tambe5
1234 SKN Sinhgad Institute of Technology & Science Lonavala, Pune, Maharashtra, India. 5Asst. Professor, SKN Sinhgad Institute of Technology & Science Lonavala, Pune, Maharashtra, India.
Published Online: November-December 2024
Pages: 20-23
Cite this article
No DOIReferences
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.
Related Articles
2024
Embedding Artificial Intelligence for Personal Voice Assistant Using NLP
2024
Analysis of Pedestrian Steel Bridge subjected the Seismic Load and Wind Load using Damper at different Span
2024
Review Paper on Comparison of Asymmetric and Symmetric RCC Building with Soil Structure Interaction by Dynamic Loading
2024
BLYNK RFID and Retinal Lock Access System
2024
ML-Driven Facial Synthesis from Spoken Words Using Conditional GANs
2024
