Glaucoma Detection Using CNN

Year : 2024 | Volume :02 | Issue : 01 | Page : 7-15
By

K.N. Brahmaji Rao,

G. Sai Yugandhar,

P. Girish,

  1. Assistant Professor, Computer Science Department Gayatri Vidya Parishad College for Degree and PG Courses, Visakhapatnam, Andhra Pradesh, India
  2. Student, Computer Science Department, Gayatri Vidya Parishad College for Degree and PG Courses. Visakhapatnam, Andhra Pradesh, India
  3. Student, Computer Science Department, Gayatri Vidya Parishad College for Degree and PG Courses. Visakhapatnam, Andhra Pradesh, India

Abstract

The word “glaucoma” refers to both the progressive loss of retinal cells within optic nerve, and the gradual loss of vision caused by optic neuropathy. A condition that affects eye vision is called glaucoma. This condition is thought to be permanent and causes visual impairment. There are no early warning signs of this glaucoma in them. The effect is so subtle that we could not even observe that your vision has changed. Today, several models have been created to accurately detect glaucoma. Thus, we describe an architecture built on deep learning and convolutional neural network for enhanced glaucoma detection. CNN can be used to differentiate among the patterns created for glaucoma and non-glaucoma. This Glaucoma Detection Web Application, patients’ retinal pictures are given and it detects the glaucoma significance and provide the results

Keywords: Glaucoma, Retinal cells, Deep learning, Optic neuropathy, Visual deterioration, Convolutional Neural Network.

[This article belongs to International Journal of Advanced Robotics and Automation Technology (ijarat)]

How to cite this article:
K.N. Brahmaji Rao, G. Sai Yugandhar, P. Girish. Glaucoma Detection Using CNN. International Journal of Advanced Robotics and Automation Technology. 2024; 02(01):7-15.
How to cite this URL:
K.N. Brahmaji Rao, G. Sai Yugandhar, P. Girish. Glaucoma Detection Using CNN. International Journal of Advanced Robotics and Automation Technology. 2024; 02(01):7-15. Available from: https://journals.stmjournals.com/ijarat/article=2024/view=171393



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References

  1. Li, Liu, et al. “A large-scale database and a CNN model for attention- based glaucoma detection.” IEEE transactions on medical imaging 39.2 (2019): 413-424. DOI: 10.1109/TMI.2019.2927226
  2. Diaz-Pinto, A. Colomer, V. Naranjo, S. Morales, Y. Xu and A. F. Frangi, “Retinal Image Synthesis and Semi-Supervised Learning for Glaucoma Assessment,” in IEEE Transactions on Medical Imaging, vol. 38, no. 9, pp. 2211-2218, Sept. 2019, Doi: 10.1109/TMI.2019.2903434.
  3. Serener and S. Serte, “Transfer Learning for Early and Advanced Glaucoma Detection with Convolutional Neural Networks,” 2019 Medical Technologies Congress (TIPTEKNO), 2019, pp. 1-4, Doi: 10.1109/TIPTEKNO.2019.8894965.
  4. Kim, Mijung, et al. “Web applicable computer-aided diagnosis of glaucoma using deep learning.” arXiv preprint arXiv:1812.02405 (2018), Doi: https://doi.org/10.48550/arXiv.1812.02405
  5. Juneja Mamta; Singh, Shaswat; Agarwal, Naman; Bali, Shivank; Gupta, Shubham; Thakur, Niharika; Jindal, Prashant (2019). Automated detection of Glaucoma using deep learning convolution network (G-net). Multimedia Tools and Applications, (), –. Doi: 10.1007/s11042-019- 7460-4.
  6. Gheisari, Soheila, et al. “A combined convolutional and recurrent neural network for enhanced glaucoma detection.” Scientific reports 11.1 (2021): 1-11. Doi: https://doi.org/10.1111/j.1442- 9071.2012.02773.x.
  7. Civit-Masot, M. J. Domínguez-Morales, S. Vicente-Díaz and A. Civit, “Dual MachineLearning System to Aid Glaucoma Diagnosis Using Disc and Cup Feature Extraction,” in IEEE Access, vol. 8, pp. 127519-127529, 2020, Doi: 10.1109/ACCESS.2020.3008539.
  8. Christopher, Mark, et al. “Effects of study population, labelling and training on glaucoma detection using deep learning algorithms.” Translational vision science & technology 9.2 (2020): 27- 27. Doi: https://doi.org/10.1167/tvst.9.2.27.
  9. George, Yasmeen, et al. “Attention-guided 3D-CNN framework for glaucoma detection and structural-functional association using volumetric images.” IEEE Journal of Biomedical and Health Informatics 24.12 (2020): 3421-3430. DOI: 10.1109/JBHI.2020.3001019.
  10. Tabassum et al., “CDED-Net: Joint Segmentation of Optic Disc and Optic Cup for Glaucoma Screening,” in IEEE Access, vol. 8, pp. 102733- 102747, 2020, Doi: 10.1109/ACCESS.2020.2998635.
  11. Tathababu Addepalli, Manish Sharma, M. Satish Kumar, Gollamudi Naveen Kumar, Prabhakara Rao Kapula, Ch. Manohar Kumar. “Self-isolated miniaturized four-port multiband 5G sub 6GHz MIMO antenna exclusively for n77/n78 & n79 wireless band applications”, Wireless Networks, 2023
  12. Rahul Krishnan at al., ‘Glaucoma Detection from Retinal Fundus Images’ Published in International Conference on Communication and Signal Processing (ICCSP) 2020, DOI: 10.1109/ICCSP48568.2020.9182388.
  13. Anita Manassakorn at al,.’GlauNet: Glaucoma Diagnosis for OCTA Imaging Using a New CNN Architecture’ Published in: IEEE Access (Volume: 10), Date of Publication: 05 September 2022 ,DOI: 10.1109/ACCESS.2022.3204029.
  14. Viswa Datta at al,. ‘Glaucoma Disease Detection Using Deep Learning’, Published in 2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT), DOI: 10.1109/ICECCT56650.2023.10179802.
  15. Nandhini at al,.‘ U-Net Architecture for Detecting Glaucoma with Retinal Fundus Images’. Published in: 2023 First International Conference on Advances in Electrical, Electronics and Computational Intelligence (ICAEECI), DOI: 10.1109/ICAEECI58247.2023.10370979.
  16. Geetanjali Gutte at al,. ‘ Detection of Glaucoma Eye Disease Using Deep Learning’.Published in: 2023 IEEE International Conference on Smart Information Systems and Technologies (SIST), DOI: 10.1109/SIST58284.2023.10223519.
Regular Issue Subscription Original Research
Volume 02
Issue 01
Received May 20, 2024
Accepted May 30, 2024
Published September 9, 2024
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