A Deep Learning Grading Classification of Diabetic Retinopathy on Retinal Fundus Images with Bio-inspired Optimization


  • Radhakrishnan Ramesh Department of Computer Application, Government Arts and Science College for Women, India
  • Selvarajan Sathiamoorthy Annamalai University PG Extension Centre, India
Volume: 13 | Issue: 4 | Pages: 11248-11252 | August 2023 | https://doi.org/10.48084/etasr.6033


Diabetic Retinopathy (DR) is considered the major cause of impaired vision for diabetic patients, particularly in developing counties. Treatment includes maintaining the patient’s present grade of vision as the illness can be irreparable. Initial recognition of DR is highly important to effectively sustain the vision of the patients. The main problem in DR recognition is that the manual diagnosis procedure consumes time, effort, and money and also includes an ophthalmologist’s analysis of retinal fundus imaging. Machine Learning (ML)-related medical image analysis is proven to be capable of evaluating retinal fundus images, and by using Deep Learning (DL) techniques. The current research presents an Automated DR detection method by utilizing the Glowworm Swarm Optimization (GSO) with Deep Learning (ADR-GSODL) approach on retinal fundus images. The main aim of the ADR-GSODL technique relies on the recognizing and classifying process of DR in retinal fundus images. To obtain this, the introduced ADR-GSODL method enforces Median Filtering (MF) as a pre-processing step. Besides, the ADR-GSODL technique utilizes the NASNetLarge method for deriving the GSO, and feature vectors are applied for parameter tuning. For the DR classification process, the Variational Autoencoder (VAE) technique is exploited. The supremacy of the ADR-GSODL approach was confirmed by a comparative simulation study. 


diabetic retinopathy screening, fundus images, deep learning, Diabetic Retinopathy (DR, metaheuristics


Download data is not yet available.


I. Kandel and M. Castelli, "Transfer Learning with Convolutional Neural Networks for Diabetic Retinopathy Image Classification. A Review," Applied Sciences, vol. 10, no. 6, Jan. 2020, Art. no. 2021.

C. Zhang, T. Lei, and P. Chen, "Diabetic Retinopathy Grading by a Source-Free Transfer Learning Approach," Biomedical Signal Processing and Control, vol. 73, Mar. 2022, Art. no. 103423.

A. K. Gangwar and V. Ravi, "Diabetic Retinopathy Detection Using Transfer Learning and Deep Learning," in Evolution in Computational Intelligence, Singapore, 2021, pp. 679–689.

D. Le et al., "Transfer Learning for Automated OCTA Detection of Diabetic Retinopathy," Translational Vision Science & Technology, vol. 9, no. 2, Jul. 2020, Art. no. 35.

M. K. Jabbar, J. Yan, H. Xu, Z. Ur Rehman, and A. Jabbar, "Transfer Learning-Based Model for Diabetic Retinopathy Diagnosis Using Retinal Images," Brain Sciences, vol. 12, no. 5, Apr. 2022, Art. no. 535.

N. E. M. Khalifa, M. Loey, M. H. N. Taha, and H. N. E. T. Mohamed, "Deep Transfer Learning Models for Medical Diabetic Retinopathy Detection," Acta Informatica Medica, vol. 27, no. 5, pp. 327–332, Dec. 2019.

N. B. Thota and D. Umma Reddy, "Improving the Accuracy of Diabetic Retinopathy Severity Classification with Transfer Learning," in 2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS), Springfield, MA, USA, Dec. 2020, pp. 1003–1006.

M. M. H. Milu, M. A. Rahman, M. A. Rashid, A. Kuwana, and H. Kobayashi, "Improvement of Classification Accuracy of Four-Class Voluntary-Imagery fNIRS Signals using Convolutional Neural Networks," Engineering, Technology & Applied Science Research, vol. 13, no. 2, pp. 10425–10431, Apr. 2023.

D. Patil and S. Jadhav, "Road Segmentation in High-Resolution Images Using Deep Residual Networks," Engineering, Technology & Applied Science Research, vol. 12, no. 6, pp. 9654–9660, Dec. 2022.

S. Rani, Y. Chabrra, and K. Malik, "An Improved Denoising Algorithm for Removing Noise in Color Images," Engineering, Technology & Applied Science Research, vol. 12, no. 3, pp. 8738–8744, Jun. 2022.

F. J. Martinez-Murcia, A. Ortiz, J. Ramírez, J. M. Górriz, and R. Cruz, "Deep residual transfer learning for automatic diagnosis and grading of diabetic retinopathy," Neurocomputing, vol. 452, pp. 424–434, Sep. 2021.

S. S, K. T, S. Bhattacharjee, D. Shahwar, and K. S. Sekhar Reddy, "Quantum Transfer Learning for Diagnosis of Diabetic Retinopathy," in 2022 International Conference on Innovative Trends in Information Technology (ICITIIT), Kottayam, India, Oct. 2022.

K. T. Islam, S. Wijewickrema, and S. O’Leary, "Identifying Diabetic Retinopathy from OCT Images using Deep Transfer Learning with Artificial Neural Networks," in 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), Cordoba, Spain, Jun. 2019, pp. 281–286.

A. Bilal, G. Sun, S. Mazhar, A. Imran, and J. Latif, "A Transfer Learning and U-Net-based automatic detection of diabetic retinopathy from fundus images," Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, vol. 10, no. 6, pp. 663–674, Nov. 2022.

N. Shaukat, J. Amin, M. Sharif, F. Azam, S. Kadry, and S. Krishnamoorthy, "Three-Dimensional Semantic Segmentation of Diabetic Retinopathy Lesions and Grading Using Transfer Learning," Journal of Personalized Medicine, vol. 12, no. 9, Sep. 2022, Art. no. 1454.

A. S. Krishnan, D. Clive R., V. Bhat, P. B. Ramteke, and S. G. Koolagudi, "A Transfer Learning Approach for Diabetic Retinopathy Classification Using Deep Convolutional Neural Networks," in 2018 15th IEEE India Council International Conference (INDICON), Coimbatore, India, Sep. 2018.

A. D. Algarni, N. Alturki, N. F. Soliman, S. Abdel-Khalek, and A. A. A. Mousa, "An Improved Bald Eagle Search Algorithm with Deep Learning Model for Forest Fire Detection Using Hyperspectral Remote Sensing Images," Canadian Journal of Remote Sensing, vol. 48, no. 5, pp. 609–620, Sep. 2022.

A. Khan, F. Aftab, and Z. Zhang, "Self-organization based clustering scheme for FANETs using Glowworm Swarm Optimization," Physical Communication, vol. 36, Oct. 2019, Art. no. 100769.

J. Saldanha, S. Chakraborty, S. Patil, K. Kotecha, S. Kumar, and A. Nayyar, "Data augmentation using Variational Autoencoders for improvement of respiratory disease classification," PLOS ONE, vol. 17, no. 8, 2022, Art. no. e0266467.

"Diabetic Retinopathy Detection," Kaggle, 2015. https://kaggle.com/competitions/diabetic-retinopathy-detection.


How to Cite

R. Ramesh and S. Sathiamoorthy, “A Deep Learning Grading Classification of Diabetic Retinopathy on Retinal Fundus Images with Bio-inspired Optimization”, Eng. Technol. Appl. Sci. Res., vol. 13, no. 4, pp. 11248–11252, Aug. 2023.


Abstract Views: 245
PDF Downloads: 191

Metrics Information