Deep Learning-Enabled Image Segmentation for Precise Retinopathy Diagnosis

Authors

  • Krishna Kant Agrawal Professor, School of Computing Science and Engineering, Galgotias University, Greater Noida, India
  • Priya Sharma Assistant Professor, School of Engineering and Technology, Sharda University, Greater Noida, UP, India
  • Gaganpreet Kaur Associate Professor, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India
  • Sarika Keswani Assistant Professor, Symbiosis Centre for Management Studies Nagpur (SCMS Nagpur) Symbiosis International (Deemed University) Pune
  • R. Rambabu Professor & HOD, Department of Computer Science & Engineering, Rajamahendri Institute of Engineering & Technology, Rajamahendravaram.
  • Subhendu Kumar Behra Assistant professor, Electronic and Telecommunication Engineering, DRIEMS University, Cuttack, Odisha
  • Kanchan Tolani Assistant Professor, Department of Management Technology, Shri Ramdeobaba College of Engineering and Management, Nagpur
  • Nitesh Singh Bhati Assistant Professor, School of Computing Science and Engineering, Galgotias University, Greater Noida, India

Keywords:

Diabetic retinopathy, Deep learning, Picture segmentation, Retinopathy diagnosis

Abstract

The risk of blindness from diabetic retinopathy is high, highlighting the need of early detection. Although manual screening is widely used, it is not without flaws due to the possibility of human mistakes. If undiagnosed and mistreated, diabetic retinopathy can compromise eyesight. Effective treatment requires early diagnosis and action. Recent improvements in deep learning and picture segmentation enhance automated retinopathy detection. Deep learning and picture segmentation will enhance retinopathy detection in this study. Retinal image segmentation techniques correctly isolate optic disc and retinal blood vessel sections. The segmentation methods can find and define retinopathy-related aberrations, facilitating diagnosis and disease progression. Automatic retinopathy diagnosis uses deep learning and picture segmentation. Retinopathy is accurately identified and classified using deep neural networks and picture segmentation. To be efficient and useful, these approaches must overcome limited annotated datasets, class imbalance, and population generalization. To confirm their usefulness, deep learning model scalability and dependability, picture segmentation techniques, and large-scale clinical investigations should be improved. Retinopathy diagnosis may be automated with deep learning and picture segmentation. These innovative tools may help physicians diagnose retinopathy earlier, improving patient outcomes and saving time on manual screening and diagnosis.

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References

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Published

12.01.2024

How to Cite

Agrawal, K. K. ., Sharma, P. ., Kaur, G. ., Keswani, S. ., Rambabu, R. ., Behra, S. K. ., Tolani, K. ., & Bhati, N. S. . (2024). Deep Learning-Enabled Image Segmentation for Precise Retinopathy Diagnosis. International Journal of Intelligent Systems and Applications in Engineering, 12(12s), 567–574. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4541

Issue

Vol. 12 No. 12s (2024)

Section

Research Article

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