Eye Disease Detection using CNN

Authors

  • B. Guna Indumathi  Computer Science and System Engineering, Lendi Institute of Engineering and Technology, Vizianagaram
  • B. Harika  Computer Science and System Engineering, Lendi Institute of Engineering and Technology, Vizianagaram
  • M. Vijay Raj  Computer Science and System Engineering, Lendi Institute of Engineering and Technology, Vizianagaram
  • G. Sharmila  Computer Science and System Engineering, Lendi Institute of Engineering and Technology, Vizianagaram
  • U. V. S. S. S. S. Kumar  Computer Science and System Engineering, Lendi Institute of Engineering and Technology, Vizianagaram
  • S. Swaroop  Computer Science and System Engineering, Lendi Institute of Engineering and Technology, Vizianagaram

DOI:

https://doi.org/10.32628/CSEIT2410137

Keywords:

CNN, Deep Learning, Machine Learning, Fundus Images, Retinal Diagnosis

Abstract

Medical professionals such as ophthalmologists frequently use fundus images, which are particularly useful in detecting different retinal problems. They used this to diagnose many eye conditions, including pathological myopia, glaucoma, cataracts, hypertension, and age-related macular degeneration. These fundus pictures can also be utilized to anticipate how severe a disease would be and to identify early warning indicators. In the field of medical science, machine learning algorithms have become increasingly important in recent times. This is also the case in the field of ophthalmology. Our goal in this work is to use deep neural networks to automatically classify retinal fundus images into healthy and pathological categories. Due to the fact that deep learning is a superb machine learning method that has shown to be incredibly accurate when applied to computer vision difficulties. Convolutional neural networks (CNNs) were employed in our study to categorize retinal pictures according to their level of health.

References

  1. Jain L., Murthy H.S., Patel C., Bansal D. Retinal eye disease detection using deep learning; Proceedings of the 2018 Fourteenth International Conference on Information Processing (ICINPRO); Bangalore, India. 21–23 December 2018; pp. 1–6.
  2. Vairamani A.D. Computational Methods and Deep Learning for Ophthalmology. Elsevier; London, UK: 2023. Detection and diagnosis of diseases by feature extraction and analysis on fundus images using deep learning techniques; pp. 211–227.
  3. Mayo Clinic Staff, Cataracts, https://www.mayoclinic.org/diseases-conditions/cataracts/symptoms-causes/syc-20353790.
  4. F. F. Allen Foster, “Vision 2020: the cataract challenge,” Community Eye Health, vol. 13, no. 34, pp. 17–19, 2000.
  5. Sneha Suresh Vanjire, Sreelakshmi, Merin Meleet. Glaucoma Disease detection using Deep learning.
  6. K. Simonyan, A. Zisserman, “Very deep convolutional networks for large-scale image recognition”, International Conference on Learning Representations, San Diego, USA, May 7-9, 2015 [9] Iwase A, Araie M, Tomidokoro A, Yamamoto T, Shimizu H, Kitazawa Y. Prevalence and causes of low vision and blindness in a Japanese adult population. The Tajimi Study. Ophthalmology. 2006; 113:1354-1362.

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Published

2024-02-29

Issue

Section

Research Articles

How to Cite

[1]
B. Guna Indumathi, B. Harika, M. Vijay Raj, G. Sharmila, U. V. S. S. S. S. Kumar, S. Swaroop, " Eye Disease Detection using CNN" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 10, Issue 1, pp.193-200, January-February-2024. Available at doi : https://doi.org/10.32628/CSEIT2410137