STM Journals Peer-reviewed research from Consortium e-Learning Network

International Journal of Advanced Robotics and Automation Technology Original Research

Glaucoma Detection Using CNN

  1. K.N. Brahmaji Rao Computer Science Department Gayatri Vidya Parishad College for Degree and PG Courses, Visakhapatnam
  2. G. Sai Yugandhar Computer Science Department, Gayatri Vidya Parishad College for Degree and PG Courses. Visakhapatnam
  3. P. Girish Computer Science Department, Gayatri Vidya Parishad College for Degree and PG Courses. Visakhapatnam

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

References (16)

  1. Li L, Xu M, Liu H, Li Y, Wang X, Jiang L, et al. A Large-Scale Database and a CNN Model for Attention-Based Glaucoma Detection. IEEE Transactions on Medical Imaging. 2020;39(2):413-424. doi:10.1109/tmi.2019.2927226
  2. Diaz-Pinto A, Colomer A, Naranjo V, Morales S, Xu Y, Frangi AF. Retinal Image Synthesis and Semi-Supervised Learning for Glaucoma Assessment. IEEE Transactions on Medical Imaging. 2019;38(9):2211-2218. doi:10.1109/tmi.2019.2903434
  3. Serener A, Serte S. Transfer Learning for Early and Advanced Glaucoma Detection with Convolutional Neural Networks. 2019 Medical Technologies Congress (TIPTEKNO). 2019:1-4. 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 J, Dominguez-Morales MJ, Vicente-Diaz S, Civit A. Dual Machine-Learning System to Aid Glaucoma Diagnosis Using Disc and Cup Feature Extraction. IEEE Access. 2020;8:127519-127529. doi:10.1109/access.2020.3008539
  8. Christopher M, Nakahara K, Bowd C, Proudfoot JA, Belghith A, Goldbaum MH, et al. Effects of Study Population, Labeling and Training on Glaucoma Detection Using Deep Learning Algorithms. Translational Vision Science & Technology. 2020;9(2):27. doi:10.1167/tvst.9.2.27
  9. George Y, Antony BJ, Ishikawa H, Wollstein G, Schuman JS, Garnavi R. Attention-Guided 3D-CNN Framework for Glaucoma Detection and Structural-Functional Association Using Volumetric Images. IEEE Journal of Biomedical and Health Informatics. 2020;24(12):3421-3430. doi:10.1109/jbhi.2020.3001019
  10. Tabassum M, Khan TM, Arsalan M, Naqvi SS, Ahmed M, Madni HA, et al. CDED-Net: Joint Segmentation of Optic Disc and Optic Cup for Glaucoma Screening. IEEE Access. 2020;8:102733-102747. 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. Krishnan R, Sekhar V, Sidharth J, Gautham S, Gopakumar G. Glaucoma Detection from Retinal Fundus Images. 2020 International Conference on Communication and Signal Processing (ICCSP). 2020:0628-0631. doi:10.1109/iccsp48568.2020.9182388
  13. Manassakorn A, Auethavekiat S, Sa-Ing V, Chansangpetch S, Ratanawongphaibul K, Uramphorn N, et al. GlauNet: Glaucoma Diagnosis for OCTA Imaging Using a New CNN Architecture. IEEE Access. 2022;10:95613-95622. doi:10.1109/access.2022.3204029
  14. Datta GV, Kishan SR, Kartik A, Sai GB, Gowtham S. Glaucoma Disease Detection Using Deep Learning. 2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT). 2023:1-6. doi:10.1109/icecct56650.2023.10179802
  15. Nandhini S, Jagadeesan J, E A. U-Net Architecture for Detecting Glaucoma with Retinal Fundus Images. 2023 First International Conference on Advances in Electrical, Electronics and Computational Intelligence (ICAEECI). 2023:1-6. doi:10.1109/icaeeci58247.2023.10370979
  16. Gutte G, Khaire B, Harne V, Shamalik R, Chippalkatti S. Detection of Glaucoma Eye Disease Using Deep Learning. 2023 IEEE International Conference on Smart Information Systems and Technologies (SIST). 2023:257-260. doi:10.1109/sist58284.2023.10223519