Abstract
Glaucoma is one of the most severe eye diseases, characterized by rapid progression and leading to irreversible blindness. It is often the case that diagnostics is carried out when one’s sight has already significantly degraded due to the lack of noticeable symptoms at early stage of the disease. Regular glaucoma screenings of the population shall improve early-stage detection, however the desirable frequency of etymological checkups is often not feasible due to the excessive load imposed by manual diagnostics on limited number of specialists. Considering the basic methodology to detect glaucoma is to analyze fundus images for the optic-disc-to-optic-cup ratio, Machine Learning algorithms can offer sophisticated methods for image processing and classification. In our work, we propose an advanced image pre-processing technique combined with a multi-view network of deep classification models to categorize glaucoma. Our Glaucoma Automated Retinal Detection Network (GARDNet) has been successfully tested on Rotterdam EyePACS AIROGS dataset with an AUC of 0.92, and then additionally fine-tuned and tested on RIM-ONE DL dataset with an AUC of 0.9308 outperforming the state-of-the-art of 0.9272. Our code is available on https://github.com/ahmed1996said/gardnet
A. Al-Mahrooqi, D. Medvedev and R. Muhtaseb—Equal contribution.
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References
Allison, K., Patel, D., Alabi, O.: Epidemiology of glaucoma: the past, present, and predictions for the future. cureus, November 2020. https://doi.org/10.7759/cureus.11686, https://www.cureus.com/articles/42672-epidemiology-of-glaucoma-the-past-present-and-predictions-for-the-future
Dibia, A.C., Nwawudu, S.E.: Automated detection of glaucoma from retinal images using image processing techniques, vol. 7, pp. 2321–9009, September 2018
Fu, H., Cheng, J., Xu, Y., Wong, D.W.K., Liu, J., Cao, X.: Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. IEEE Trans. Med. Imaging 37(7), 1597–1605 (2018). https://doi.org/10.1109/tmi.2018.2791488
Fu, H., et al.: Refuge: retinal fundus glaucoma challenge (2019). https://doi.org/10.21227/tz6e-r977, https://dx.doi.org/10.21227/tz6e-r977
Fumero, F., Diaz-Aleman, T., Sigut, J., Alayón, S., Arnay, R., Angel-Pereira, D.: Rim-one dl: a unified retinal image database for assessing glaucoma using deep learning. Image Anal. Stereology 39 (2020). https://doi.org/10.5566/ias.2346
Fumero, F., Sigut, J., Alayón, S., González-Hernández, M., González de la Rosa, M.: Interactive tool and database for optic disc and cup segmentation of stereo and monocular retinal fundus images, July 2015
Fumero, F., Sigut, J., Alayón, S., González-Hernández, M., González de la Rosa, M.: Interactive tool and database for optic disc and cupsegmentation of stereo and monocular retinal fundus images, June 2015
Lee, D.A., Higginbotham, E.J.: Glaucoma and its treatment: a review. Am. J. Health-Syst. Pharm. 62(7), 691–699 (2005). https://doi.org/10.1093/ajhp/62.7.691, https://academic.oup.com/ajhp/article/62/7/691/5134357
Lee, J., Lee, J., Song, H., Lee, C.: Development of an end-to-end deep learning system for glaucoma screening using color fundus images. JAMA Ophthalmol. 137, 1353–1360 (2019)
Maadi, F., Faraji, N., Bibalan, M.H.: A robust glaucoma screening method for fundus images using deep learning technique. In: 2020 27th National and 5th International Iranian Conference on Biomedical Engineering (ICBME), pp. 289–293. IEEE, Tehran, Iran, November 2020. https://doi.org/10.1109/ICBME51989.2020.9319434, https://ieeexplore.ieee.org/document/9319434/
Phasuk, S., et al.: Automated glaucoma screening from retinal fundus image using deep learning. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 904–907 (2019). https://doi.org/10.1109/EMBC.2019.8857136
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation (2015). https://doi.org/10.48550/ARXIV.1505.04597, https://arxiv.org/abs/1505.04597
Selvaraju, R.R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., Batra, D.: Grad-cam: why did you say that? visual explanations from deep networks via gradient-based localization. CoRR abs/1610.02391 (2016). http://arxiv.org/abs/1610.02391
Sevastopolsky, A.: Optic disc and cup segmentation methods for glaucoma detection with modification of u-net convolutional neural network. Pattern Recogn. Image Anal. 27(3), 618–624 (2017). https://doi.org/10.1134/s1054661817030269, https://doi.org/10.1134
Sivaswamy, J., Krishnadas, S.R., Datt Joshi, G., Jain, M., Syed Tabish, A.U.: Drishti-GS: retinal image dataset for optic nerve head (onh) segmentation. In: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), pp. 53–56 (2014). https://doi.org/10.1109/ISBI.2014.6867807
Sreng, S., Maneerat, N., Hamamoto, K., Win, K.Y.: Deep learning for optic disc segmentation and glaucoma diagnosis on retinal images. Appl. Sci. 10(14), 4916 (2020). https://doi.org/10.3390/app10144916, https://www.mdpi.com/2076-3417/10/14/4916
Coen de Vente, Koenraad A. Verrmeer, N.J.: Airogs: artificial intelligence for robust glaucoma screening challenge. In: IEEE International Symposium on Biomedical Imaging. IEEE (2022)
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Al-Mahrooqi, A., Medvedev, D., Muhtaseb, R., Yaqub, M. (2022). GARDNet: Robust Multi-view Network for Glaucoma Classification in Color Fundus Images. In: Antony, B., Fu, H., Lee, C.S., MacGillivray, T., Xu, Y., Zheng, Y. (eds) Ophthalmic Medical Image Analysis. OMIA 2022. Lecture Notes in Computer Science, vol 13576. Springer, Cham. https://doi.org/10.1007/978-3-031-16525-2_16
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