Diabetic Retinopathy (DR) is an eye condition that arises from diabetes-related complications, making early detection essential for effective treatment. As diabetes advances, an individual’s vision may deteriorate, potentially resulting in diabetic retinopathy. This condition is divided into two primary types: Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR). Early treatment to maintain a strategic distance from a change less visual impedance is basic as it may save diabetic retinopathy vision. With moves in computer science strategies, openings for the disclosure of DR at the early stages have extended. The chances of recovery will be increased by employing the techniques. In our proposed model, we employed the VGG16 along with the transfer learning techniques for extracting the features from the fundus images. Deep learning techniques, like Convolutional Neural Networks (CNN), DenseNet and machine learning techniques like multivariate decision tree and multiclass SVM ensemble classifier, can be used for classification. The results performed can be evaluated using f1 score, precision, recall, and accuracy.

Keywords: Diabetic Retinopathy, permanent blindness, Deep Learning, Convolutional Neural Networks

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1. Al-Smadi M, Hammad M, Baker QB, Sa’ad A. A transfer learning with deep neural network approach for diabetic retinopathy classification. IntiJ Electr Comp Eng. 2021 Aug 1;11(4):3492.
2. Khalifa NE, Loey M, Taha MH, Mohamed HN. Deep transfer learning models for medical diabetic retinopathy detection. Acta Informatica Medica. 2019 Dec;27(5):327.
3. Kandel I, Castelli M. Transfer learning with convolutional neural networks for diabetic retinopathy image classification. A review. Applied Sci. 2020 Mar 16;10(6):2021.
4. Sarki R, Ahmed K, Wang H, Zhang Y. Automatic detection of diabetic eye disease through deep learning using fundus images: a survey. IEEE access. 2020 Aug 10;8:151133–49.
5. Jabbar MK, Yan J, Xu H, Ur Rehman Z, Jabbar A. Transfer learning-based model for diabetic retinopathy diagnosis using retinal images. Brain Sciences. 2022 Apr 22;12(5):535.
6. Bijam P, Deshmukh S. A review on detection of diabetic retinopathy using deep learning and transfer learning-based strategies. Intl J Comp (IJC). 2023;45(1):164–75.
7. Kuna SL, Krishna Prasad AV. Deep learning empowered diabetic retinopathy detection and classification using retinal fundus images. Int J Recent Innov Trends Comput Commun. 2023;11(1):117–27. DOI: https://doi.org/10.17762/ijritcc.v11i1.6058
8. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2017 (pp. 4700–08).
9. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. 2014 Sep 4.
10. Nalawade PK, Bhujbal S, Lahase C, Mhasade S, Tambe K. Smart road damage detection and alarms. Int Res J Modernization Eng Technol Sci. 2023;5(6):254–62.

Diabetic Retinopathy (DR) is an eye condition that arises from diabetes-related complications, making early detection essential for effective treatment. As diabetes advances, an individual’s vision may deteriorate, potentially resulting in diabetic retinopathy. This condition is divided into two primary types: Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR). Early treatment to maintain a strategic distance from a change less visual impedance is basic as it may save diabetic retinopathy vision. With moves in computer science strategies, openings for the disclosure of DR at the early stages have extended. The chances of recovery will be increased by employing the techniques. In our proposed model, we employed the VGG16 along with the transfer learning techniques for extracting the features from the fundus images. Deep learning techniques, like Convolutional Neural Networks (CNN), DenseNet and machine learning techniques like multivariate decision tree and multiclass SVM ensemble classifier, can be used for classification. The results performed can be evaluated using f1 score, precision, recall, and accuracy.

Keywords: Diabetic Retinopathy, permanent blindness, Deep Learning, Convolutional Neural Networks

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Al-Smadi M, Hammad M, Baker QB, Sa’ad A. A transfer learning with deep neural network approach for diabetic retinopathy classification. IntiJ Electr Comp Eng. 2021 Aug 1;11(4):3492.
2. Khalifa NE, Loey M, Taha MH, Mohamed HN. Deep transfer learning models for medical diabetic retinopathy detection. Acta Informatica Medica. 2019 Dec;27(5):327.
3. Kandel I, Castelli M. Transfer learning with convolutional neural networks for diabetic retinopathy image classification. A review. Applied Sci. 2020 Mar 16;10(6):2021.
4. Sarki R, Ahmed K, Wang H, Zhang Y. Automatic detection of diabetic eye disease through deep learning using fundus images: a survey. IEEE access. 2020 Aug 10;8:151133–49.
5. Jabbar MK, Yan J, Xu H, Ur Rehman Z, Jabbar A. Transfer learning-based model for diabetic retinopathy diagnosis using retinal images. Brain Sciences. 2022 Apr 22;12(5):535.
6. Bijam P, Deshmukh S. A review on detection of diabetic retinopathy using deep learning and transfer learning-based strategies. Intl J Comp (IJC). 2023;45(1):164–75.
7. Kuna SL, Krishna Prasad AV. Deep learning empowered diabetic retinopathy detection and classification using retinal fundus images. Int J Recent Innov Trends Comput Commun. 2023;11(1):117–27. DOI: https://doi.org/10.17762/ijritcc.v11i1.6058
8. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2017 (pp. 4700–08).
9. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. 2014 Sep 4.
10. Nalawade PK, Bhujbal S, Lahase C, Mhasade S, Tambe K. Smart road damage detection and alarms. Int Res J Modernization Eng Technol Sci. 2023;5(6):254–62.