Diabetic Retinopathy: Evaluation and Analysis using Computer Method

Authors

  • Anil Kumar
  • Shelly Kataria
  • Anita Singh Banafar
  • Rashmi Mishra
  • Sushil Shukla

Keywords:

Neural Networks, Convolutional Neural Networks, Retinopathy diabetes, Prediction, Analysis, CNN

Abstract

This study suggests novel approaches for solving retinopathy diabetes through computational methods. Diabetes is the primary cause of this widespread retinal infection, which is a leading cause of vision impairment in middle-aged and older adults. In order to successfully monitor the infection's course, early identification and detection by routine screening and appropriate treatment would be highly advantageous. Retinopathy is now detected via a labor-intensive, time-consuming process that mostly depends on a doctor's skill. Automated detection of diabetic retinopathy is required to overcome these problems. Diagnosis of diabetic retinopathy also depends on early detection because, with appropriate treatment, it can prevent blindness.

In order to recommend lone sick individuals to a master for extra care and supervision, a robotized prediction framework for diabetic retinopathy upgrades in the eye is necessary because there are more people with the condition than eye experts who can scan them. Shaded photographs of the retina are used to analyze the angles and stages of retinopathy. An great tool for compelling Diabetic Retinopathy screening is image recognition, which may be used to detect these many symptoms and phases of the disease in a robotized manner. Additionally, it can send the patient to a professional for aid. A unique approach that detects the early indicators of diabetic retinopathy and classifies them into distinct groups is proposed in this work.

This method can be used to confirm or disprove the detection in medical situations. The approach created here could also be used to combine detecting expertise for the benefit of humanity. The model gave the highest possible accuracy. Therefore, a plan that has been implemented may help people with diabetic retinopathy avoid becoming completely blind.

Downloads

Download data is not yet available.

References

Shegdar, S., Bhatlavande, A., Patil, D., & Kadam, S. (2021): Automated Early Detection of Diabetic Retinopathy. In Techno-Societal 2020, pp. 897-904, Springer publication.

Oda, M., Yamaguchi, T., Fukuoka, H., Ueno, Y., & Mori, K. (2020): Automated eye disease classification method from anterior eye image using anatomical structure focused image classification technique, Proceedings of SPIE Medical Imaging 2020: Computer-Aided Diagnosis, Vol.11314, 1131446.

Meiburger, K. M., Acharya, U. R., & Molinari, F. (2018): Automated localization and segmentation techniques for B-mode ultrasound images: A review. Computers in biology and medicine, vol. 92, pp 210-235.

Qiao, L., Zhu, Y., & Zhou, H. (2020): Diabetic retinopathy detection using prognosis of micro aneurysm and early diagnosis system for non-proliferative diabetic retinopathy based on deep learning algorithms. IEEE Access, vol. 8, pp 104292-104302.

Abbas, S. M., & Singh, S. N. (2018):Region-based object detection and classification using faster R-CNN, 4th International Conference on Computational Intelligence & Communication Technology pp. 1-6, IEEE. 2018.

Yu, K.-H., Beam, A. L. & Kohane, I. S. Artificial intelligence in health care. Nat. Biomed. Eng.2, 719–731 (2018).

Ting DSW, Cheung CY, Lim G, et al.(2017): Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes. JAMA.,318(22):2211–2223.

A. Kwasigroch, A. Mikolajczyk and M. Grochowski (2017): Deep convolutional neural networks as a decision support tool in medical problems - malignant melanoma case study, Trends in Advanced Intelligent Control Optimization and Automation, pp. 848-856.

Y. LeCun, K. Kavukcuoglu and C. Farabet (2010): Convolutional networks and applications in vision", Proceedings of 2010 IEEE International Symposium on Circuits and Systems, pp. 253-256.

V. Ramya, R. Jayaparvathy (2023): A New Technique using Deep Learning to Segment Retinal Blood Vessels, 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS), pp.1405-1410.

Jiajia Li, Chenhao Lin, Zikai Wang, Haitao Song, Feng Tan, Lun Hu, Pengwei Hu,(2023) : MLGL: Model-free Lesion Generation and Learning for Diabetic Retinopathy Diagnosis, 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp.1266-1271.

Dharsinala Harikrishna, Naluguru Udaya Kumar (2022): Artificial Intelligence System for Classification of Diabetic Retinopathy", 2022 Smart Technologies, Communication and Robotics (STCR), pp.1-5..

T. Rajasekar, Albert Joshwa A, M Deepa, A I Kaaviya(2022):Graphical User Interface for Classification of Diabetic Retinopathy using Image Processing", 2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA), pp.1789-1796.

Marcello D. Giammarco, G. Iadarola, F. Martinelli, F. Mercaldo, A. S.(2022):Explainable Retinopathy Diagnosis and Localisation by means of Class Activation Mapping", 2022 International Joint Conference on Neural Networks (IJCNN), pp.1-8.

Sashank S., Sowmya S. (2020): Detection and Prognosis Evaluation of Diabetic Retinopathy using Ensemble Deep Convolutional Neural Networks", 2020 International Electronics Symposium (IES), pp.78-85.

Sheena P.; Sindhu K.; Vishalbalaji S., Saranya J; Palanivelan M.(2023) :Efficient CNN based detection of diabetic retinopathy, AIP Conf. Proc. 2829, 060001.

Reguant, R., Brunak, S. and Saha, S. (2021): Understanding inherent image features in CNN-based assessment of diabetic retinopathy. Sci Rep11, 9704.

Haq, N.U., Waheed, T., Ishaq, Hassan, MA. Safie. N , , NF and Shoaib M :Computationally efficient deep learning models for diabetic retinopathy detection : a systematic literature review. Artif Intell Rev 57, 309 (2024) .

Alicia J. Jenkins, Mugdha V. Joglekar, Anandwardhan A. Hardikar, Anthony C. Keech, David N. O'Neal, S. Andrzej, Januszewski Biomarkers in diabetic retinopathy, Rev. Diabet. Stud.: Reg. Dev. Stud., 12 (1–2) (2015), p. 159.

Downloads

Published

2025-07-10

How to Cite

1.
Kumar A, Kataria S, Banafar AS, Mishra R, Shukla S. Diabetic Retinopathy: Evaluation and Analysis using Computer Method. J Neonatal Surg [Internet]. 2025Jul.10 [cited 2025Oct.1];14(32S):4739-47. Available from: https://www.jneonatalsurg.com/index.php/jns/article/view/8187

Issue

Vol. 14 No. 32S (2025): Journal of Neonatal Surgery

Section

Original Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

You are free to:

  • Adapt — remix, transform, and build upon the material for any purpose, even commercially.

Terms:

  • Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  • No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.