Skip to main content
SpringerLink
Log in

An Intelligent Optimized Deep Network for Retinopathy Diabetes Segmentation

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Retinopathy diabetes is the most interesting field in the optical world for improving vision-related problems. However, it is difficult to forecast the affected part and its types in the retinal image because of improper analyzing features. So, the current work intended to develop a novel Hybrid Horse-herd based Convolutional ResNet Segmentation Framework (HHbCRSF) for predicting the extraction of diabetes cells in the retinal images. Initially, the noise filtering function was activated in the hidden layer of the HHbCRSF to remove the noise parameters. Then the refined data is taken as the input for the classification phase for analyzing and tracking the affected features. Finally, the affected region was segmented, and the types were classified. This planned system is implemented in the Python framework. The successive score was measured as Accuracy, error rate, sensitivity, and specificity. In that, the newly developed design scored the maximum Accuracy and less misclassification score for every disease.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data Availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. Kadan, A. B., & Subbian, P. S. (2021). Diabetic retinopathy detection from fundus images using machine learning techniques: A review. Wireless Personal Communications, 121, 2199–2212. https://doi.org/10.1007/s11277-021-08817-1

    Article  Google Scholar 

  2. Sathananthavathi, V., & Indumathi, G. (2022). Dilated deep neural architectures for improving retinal vessel extraction. Wireless Personal Communications, 125, 3641–3659. https://doi.org/10.1007/s11277-022-09728-5

    Article  Google Scholar 

  3. Ravindraiah, R., & Reddy, S. C. M. (2020). An instinctive application of spatially weighted possibilistic clustering methods for the detection of lesions in diabetic retinopathy images in multi-dimensional kernel space. Wireless Personal Communications, 113, 223–240. https://doi.org/10.1007/s11277-020-07186-5

    Article  Google Scholar 

  4. Burhan, A. M., Klahan, B., Cummins, W., Andrés-Guerrero, V., Byrne, M. E., O’Reilly, N. J., Chauhan, A., Fitzhenry, L., & Hughes, H. (2021). Posterior segment ophthalmic drug delivery: Role of muco-adhesion with a special focus on chitosan. Pharmaceutics, 13(10), 1685. https://doi.org/10.3390/pharmaceutics13101685

    Article  Google Scholar 

  5. Rathnasamy, G., Foulds, W. S., Ling, E. A., & Kaur, C. (2019). Retinal microglia–A key player in healthy and diseased retina. Progress in Neurobiology, 173, 18–40. https://doi.org/10.1016/j.pneurobio.2018.05.006

    Article  Google Scholar 

  6. Maya, K. V., & Adarsh, K. S. (2019). Detection of retinal lesions based on deep learning for diabetic retinopathy. In 2019 5th international conference on electrical energy systems (ICEES). IEEE. https://doi.org/10.1109/ICEES.2019.8719242

  7. Sinha, S., Saxena, S., & Jain, S. (2021). Trends of accuracy on diabetic retinopathy considering epoch effect and Bayes error. In 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON). IEEE. https://doi.org/10.1109/GUCON50781.2021.9573967

  8. Kadhim, K. T., Alsahlany, A. M., Wadi, S. M., & Kadhum, H. T. (2020). An overview of patient’s health status monitoring system based on Internet of Things (IoT). Wireless Personal Communications, 114(3), 2235–2262. https://doi.org/10.1007/s11277-020-07474-0

    Article  Google Scholar 

  9. Huda, S. M. A., Ila, I. J., Sarder, S., Shamsujjoha, M., & Ali, M. N. Y. (2019). An improved approach for detection of diabetic retinopathy using feature importance and machine learning algorithms. In 2019 7th International Conference on Smart Computing & Communications (ICSCC). IEEE. https://doi.org/10.1109/ICSCC.2019.8843676

  10. Nweke, H. F., The, Y. W., Al-Garadi, M. A., & Alo, U. R. (2018). Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges. Expert Systems with Applications, 105, 233–261. https://doi.org/10.1016/j.eswa.2018.03.056

    Article  Google Scholar 

  11. Porwal, P., Pachade, S., Kokare, M., Deshmukh, G., Son, J., Bae, W., Liu, L., Wang, J., Liu, X., Gao, L., Wu, T., Xiao, J., Wang, F., Yin, B., Wang, Y., Danala, G., & He, L. (2020). Idrid: Diabetic retinopathy–segmentation and grading challenge. Medical Image Analysis, 59, 101561. https://doi.org/10.1016/j.media.2019.101561

    Article  Google Scholar 

  12. Sabanayagam, C., Banu, R., Chee, M. L., Lee, R., Wang, Y. X., Tan, G., Jonas, J. B., Lamoureux, E. L., Cheng, C. Y., Klein, B. E. K., Mitchell, P., Klein, R., Cheung, C. M. G., & Wong, T. Y. (2019). Incidence and progression of diabetic retinopathy: A systematic review. The Lancet Diabetes & Endocrinology, 7(2), 140–149. https://doi.org/10.1016/S2213-8587(18)30128-1

    Article  Google Scholar 

  13. Vellido, A. (2020). The importance of interpretability and visualization in machine learning for applications in medicine and health care. Neural Computing and Applications, 32(24), 18069–18083. https://doi.org/10.1007/s00521-019-04051-w

    Article  Google Scholar 

  14. Panesar, A. (2019). Machine learning and AI for healthcare. Coventry. Apress.

    Book  Google Scholar 

  15. Fukumura, Y. E., Gray, J. M. L., Lucas, G. M., Becerik-Gerber, B., & Roll, S. C. (2021). Worker perspectives on incorporating artificial intelligence into office workspaces: Implications for the future of office work. International Journal of Environmental Research and Public Health, 18(4), 1690. https://doi.org/10.3390/ijerph18041690

    Article  Google Scholar 

  16. Schiff, D. (2021). Out of the laboratory and into the classroom: The future of artificial intelligence in education. AI & Society, 36(1), 331–348. https://doi.org/10.1007/s00146-020-01033-8

    Article  Google Scholar 

  17. Vujosevic, S., Aldington, S. J., Silva, P., Hernández, C., Scanlon, P., Peto, T., & Simó, R. (2020). Screening for diabetic retinopathy: New perspectives and challenges. The Lancet Diabetes & Endocrinology, 8(4), 337–347. https://doi.org/10.1016/S2213-8587(19)30411-5

    Article  Google Scholar 

  18. Hogue, H., & Lalwani, N. (2022). Magnetic Resonance Imaging Defecography: The Role of the Specialist Nurse. Journal of Radiology Nursing. https://doi.org/10.1016/j.jradnu.2022.05.008

    Article  Google Scholar 

  19. Fujita, H. (2020). AI-based computer-aided diagnosis (AI-CAD): The latest review to read first. Radiological Physics and Technology, 13(1), 6–19. https://doi.org/10.1007/s12194-019-00552-4

    Article  Google Scholar 

  20. Arrigo, A., Aragona, E., Parodi, M. B., & Bandello, F. (2022). Quantitative approaches in multimodal fundus imaging: State of the art and future perspectives. Progress in Retinal and Eye Research. https://doi.org/10.1016/j.preteyeres.2022.101111

    Article  Google Scholar 

  21. Nagaraj, P., Muneeswaran, V., Reddy, L. V., Upendra, P., & Reddy, M. V. V. (2020). Programmed multi-classification of brain tumor images using deep neural network. In 2020 4th international conference on intelligent computing and control systems (ICICCS). IEEE. https://doi.org/10.1109/ICICCS48265.2020.9121016

  22. Samudre, P., Shende, P., & Jaiswal, V. (2019). Optimizing performance of convolutional neural network using computing technique. In 2019 IEEE 5th international conference for convergence in technology (I2CT). IEEE. https://doi.org/10.1109/I2CT45611.2019.9033876

  23. Barges, E., & Thabet, E. (2022). GLDM and Tamura features based KNN and particle swarm optimization for automatic diabetic retinopathy recognition system. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-022-13282-4

    Article  Google Scholar 

  24. Shankar, K., Zhang, Y., Liu, Y., Wu, L., & Chen, C. H. (2020). Hyperparameter tuning deep learning for diabetic retinopathy fundus image classification. IEEE Access, 8, 118164–118173. https://doi.org/10.1109/ACCESS.2020.3005152

    Article  Google Scholar 

  25. Das, S., Kharbanda, K., Suchetha, M., Raman, R., & Dhas, E. (2021). Deep learning architecture based on segmented fundus image features for classification of diabetic retinopathy. Biomedical Signal Processing and Control, 68, 102600. https://doi.org/10.1016/j.bspc.2021.102600

    Article  Google Scholar 

  26. Aujih, A. B., Izhar, L. I., Mériaudeau, F., & Shapiai, M. I. (2018). Analysis of retinal vessel segmentation with deep learning and its effect on diabetic retinopathy classification. In 2018 International conference on intelligent and advanced system (ICIAS), IEEE. https://doi.org/10.1109/ICIAS.2018.8540642

  27. Shankar, K., Sait, A. R. W., Gupta, D., Lakshmanaprabu, S. K., Khanna, A., & Pandey, H. M. (2020). Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model. Pattern Recognition Letters, 133, 210–216. https://doi.org/10.1016/j.patrec.2020.02.026

    Article  Google Scholar 

  28. Li, X., Shen, L., Shen, M., Tan, F., & Qiu, C. S. (2019). Deep learning based early stage diabetic retinopathy detection using optical coherence tomography. Neurocomputing, 369, 134–144. https://doi.org/10.1016/j.neucom.2019.08.079

    Article  Google Scholar 

  29. Sarvamangala, D. R., & Kulkarni, R. V. (2021). Convolutional neural networks in medical image understanding: A survey. Evolutionary intelligence. https://doi.org/10.1007/s12065-020-00540-3

    Article  Google Scholar 

  30. Mohapatra, S., Abhishek, N. V. S., Bardhan, D., Ghosh, A. A., & Mohanty, S. (2021). Comparison of MobileNet and ResNet CNN Architectures in the CNN-Based Skin Cancer Classifier Model. Machine Learning for Healthcare Applications. https://doi.org/10.1002/9781119792611.ch11

    Article  Google Scholar 

  31. MiarNaeimi, F., Azizyan, G., & Rashki, M. (2021). Horse herd optimization algorithm: A nature-inspired algorithm for high-dimensional optimization problems. Knowledge-Based Systems, 213, 106711. https://doi.org/10.1016/j.knosys.2020.106711

    Article  Google Scholar 

  32. Abdulsahib, A. A., Mahmoud, M. A., Mohammed, M. A., Rasheed, H. H., Mostafa, S. A., & Maashi, M. S. (2021). Comprehensive review of retinal blood vessel segmentation and classification techniques: Intelligent solutions for green computing in medical images, current challenges, open issues, and knowledge gaps in fundus medical images. Network Modeling Analysis in Health Informatics and Bioinformatics, 10(1), 1–32. https://doi.org/10.1007/s13721-021-00294-7

    Article  Google Scholar 

  33. Kumar, S., & Kumar, B. (2018). Diabetic retinopathy detection by extracting area and number of microaneurysm from colour fundus image. In 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN), IEEE. DOI: https://doi.org/10.1109/SPIN.2018.8474264

Download references

Acknowledgements

None.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, VIT-AP University, Amaravati, 522237, India

    M. Gargi & Anupama Namburu

Authors
  1. M. Gargi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anupama Namburu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Gargi.

Ethics declarations

Conflict of interest

The authors declare that they have no potential conflict of interest.

Ethical Approval

All applicable institutional and/or national guidelines for the care and use of animals were followed.

Informed Consent

For this type of analysis formal consent is not needed.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gargi, M., Namburu, A. An Intelligent Optimized Deep Network for Retinopathy Diabetes Segmentation. Wireless Pers Commun 135, 1885–1907 (2024). https://doi.org/10.1007/s11277-024-11184-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-024-11184-2

Keywords

Search

Navigation