Deep Neural Network Approach for Detection of Glaucoma, Cataract, and Diabetic Retinopathy
Keywords:
Cataract, Convolutional Neural Networks (CNN), Deep learning, Diabetic retinopathy, Eye disease detection, Glaucoma, InceptionV3, ResNet-50, Retinal image classificationAbstract
Glaucoma, a group of eye conditions leading to optic nerve damage, is among the primary causes of irreversible blindness globally. Early diagnosis is critical to prevent progressive vision loss. This paper explores a deep learning-based method to detect glaucoma using fundus images. Leveraging Convolutional Neural Networks (CNNs), the system is trained to distinguish between glaucomatous and non-glaucomatous eyes with high accuracy. A curated dataset of labeled retinal images was used for training and validation. The model's performance was assessed using key metrics such as accuracy, precision, recall, and F1-score, demonstrating that deep learning techniques can significantly enhance the detection and classification of glaucoma. This approach offers a scalable and efficient alternative for clinical decision support in ophthalmology.
References
V. Gulshan, L. Peng, M. Coram, “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs,” JAMA, vol. 316, no. 22, p. 2402, Dec. 2016, doi: https://doi.org/10.1001/jama.2016.17216.
H. Pratt, F. Coenen, D. M. Broadbent, S. P. Harding, and Y. Zheng, “Convolutional Neural Networks for Diabetic Retinopathy,” Procedia Computer Science, vol. 90, pp. 200–205, 2016, doi: https://doi.org/10.1016/j.procs.2016.07.014.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826, Jun. 2016, doi: https://doi.org/10.1109/cvpr.2016.308.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, Jun. 2016, doi: https://doi.org/10.1109/cvpr.2016.90.
D. S. W. Ting , L. R. Pasquale ,L. Peng, “Artificial intelligence and deep learning in ophthalmology,” British Journal of Ophthalmology, vol. 103, no. 2, pp. 167–175, Oct. 2018, doi: https://doi.org/10.1136/bjophthalmol-2018-313173.
M. D. Abràmoff, P. T. Lavin, M. Birch, N. Shah, and J. C. Folk, “Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices,” npj Digital Medicine, vol. 1, no. 1, Aug. 2018, doi: https://doi.org/10.1038/s41746-018-0040-6.
R. Gargeya and T. Leng, “Automated Identification of Diabetic Retinopathy Using Deep Learning,” Ophthalmology, vol. 124, no. 7, pp. 962–969, Jul. 2017, doi: https://doi.org/10.1016/j.ophtha.2017.02.008.
Z. Li , C. Guo , D. Nie , “Deep learning for detecting retinal detachment and discerning macular status using ultra-widefield fundus images,” Communications Biology, vol. 3, no. 1, Jan. 2020, doi: https://doi.org/10.1038/s42003-019-0730-x.
D. S. Kermany , M. Goldbaum , W. Cai , C.S. Valentim , “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,” Cell, vol. 172, no. 5, pp. 1122-1131.e9, Feb. 2018, doi: https://doi.org/10.1016/j.cell.2018.02.010.
R. Rajalakshmi, R. Subashini, R. M. Anjana, and V. Mohan, “Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence,” Eye, vol. 32, no. 6, pp. 1138–1144, Mar. 2018, doi: https://doi.org/10.1038/s41433-018-0064-9.
G. Quellec, M. Lamard, M.D. Abràmoff,“A multiple-instance learning framework for diabetic retinopathy screening,” Medical Image Analysis, vol. 16, no. 6, pp. 1228–1240, Aug. 2012, doi: https://doi.org/10.1016/j.media.2012.06.003.
L. Giancardo, F. Meriaudeau, T. P. Karnowski, “Exudate-based diabetic macular edema detection in fundus images using publicly available datasets,” Medical Image Analysis, vol. 16, no. 1, pp. 216–226, Jan. 2012, doi: https://doi.org/10.1016/j.media.2011.07.004.
G. Quellec, M. Lamard, P. M. Josselin, G. Cazuguel, Bé. Cochener, and C. Roux, “Optimal Wavelet Transform for the Detection of Microaneurysms in Retina Photographs,” IEEE Transactions on Medical Imaging, vol. 27, no. 9, pp. 1230–1241, 2008, doi: https://doi.org/10.1109/TMI.2008.920619.
S. Philip, A. D. Fleming, K. A. Goatman, “The efficacy of automated ‘disease/no disease’ grading for diabetic retinopathy in a systematic screening programme,” British Journal of Ophthalmology, vol. 91, no. 11, pp. 1512–1517, May 2007, doi: https://doi.org/10.1136/bjo.2007.119453
M. R. K. Mookiah, U. R. Acharya, C. K. Chua, C. M. Lim, E. Y. K. Ng, and A. Laude, “Computer-aided diagnosis of diabetic retinopathy: A review,” Computers in Biology and Medicine, vol. 43, no. 12, pp. 2136–2155, Dec. 2013, doi: https://doi.org/10.1016/j.compbiomed.2013.10.007.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, no. 7553, pp. 436–444, May 2015, doi: https://doi.org/10.1038/nature14539.
J. Dean, G. Corrado, R. Monga, K. Chen, “Large Scale Distributed Deep Networks,” Neural Information Processing Systems, 2012. https://papers.nips.cc/paper_files/paper/2012/hash/6aca97005c68f1206823815f66102863-Abstract.html
