By: Purushotam Naidu K., G. Sri Sivaanandini, P. Divya, Ch. AnuSri, T. Durga Prasanna, and R. Naga Supriya
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 the 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 performed results can be evaluated using f1 score, precision, recall, accuracy.
Keywords: Diabetic Retinopathy, Permanent blindness, Deep Learning, Convolutional Neural Networks
Citation:
Refrences:
- Al-Smadi M, Hammad M, Baker QB, Sa’ad A. A transfer learning with deep neural network approach for diabetic retinopathy classification. International Journal of Electrical and Computer Engineering. 2021 Aug 1;11(4):3492.
- 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.
- Kandel I, Castelli M. Transfer learning with convolutional neural networks for diabetic retinopathy image classification. A review. Applied Sciences. 2020 Mar 16;10(6):2021.
- 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.
- 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.
- Bijam P, Deshmukh S. A review on detection of diabetic retinopathy using deep learning and transfer learning based strategies. International Journal of Computer (IJC). 2023;45(1):164-75.
- Sril Lxmi Kuna, A.V. Krishna Prasad. Deep Learning Empowered Diabetic Retinopathy Detection and Classification using Retinal Fundus Images. International Journal on Recent and Innovation Trends in Computing and Communication. 2023;11(1): 117–127. DOI: https://doi.org/10.17762/ijritcc.v11i1.6058
- Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. InProceedings of the IEEE conference on computer vision and pattern recognition 2017 (pp. 4700-4708).
- Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. 2014 Sep 4.
- Dr. Kamini Nalawade, Suyog Bhujbal, Chetan Lahase, Suvita Mhasade, Krishna Tambe. Smart Road Damage Detection and Alarms. International Research Journal of Modernization in Engineering Technology and Science. 2023;05(06):254-262.
