Abstract
Diabetic Retinopathy (DR) is one of the biggest eye diseases affecting the worldwide population. The DR presents several damages in the retina depending on its grade of advance, although the damages are asymptomatic in almost all cases. The presence of neovascularization (NV) is considered as the worse stage in the DR, and the patients of this stage require urgent treatment to avoid partial or total blindness. Timely detection of the new vessels in the retina can lead to an adequate treatment to avoid vision loss. In this work, we present automatic detection of neovascularization in the optic disc region (NVD) using a deep learning algorithm. We evaluate several deep neural networks (DNNs) to classify between health optic disc region and NVD. The better DNNs are DenseNet-161 and Efficientnet-B7, which show 93.3% and 92.0% accuracy and 89.5% and 84.2% in sensitivity, respectively. In the computational complexity, DenseNet-161 has a lower number of trainable parameters than that of Efficientnet-B7. To train the DNNs appropriately, we construct a labeled dataset from one of the largest public datasets, bounding NV regions in the retinal images.
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References
Asociación Mexicana de Retina: Retinopatía diabetíca. https://amretina.org/info01.html. Accessed 18 Feb 2021
ICO Guidelines for Diabetic Eye Care. https://www.icoph.org. Accessed 18 Feb 2021
Campochiaro, P.: Ocular neovascularization. J. Mol. Med. (Berlin, Germany) 91(3), 311–21 (2013)
Litjens, G., et al.: A survey on deep learning in medical image. Med. Image Anal. 42(12), 60–88 (2017)
Tajbakhsh, N., et al.: Convolutional neural networks for medical image analysis: full training or fine tuning. IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016)
Calderon, G., et al.: A teleophthalmology support system based on the visibility of retinal elements using the CNNs. Sensors 20, 2838 (2020)
Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA J. Am. Med. Assoc. 316(22), 2402–2410 (2016)
Oh, K., Kong, H., Leem, D., Lee, H., Seo, H., Yoon, S.: Early detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images. Sci. Rep. 11(1), 1–9 (2021)
Vega, R., et al.: Retinal vessel extraction using Lattice Neural Networks with dendritic processing. Comput. Biol. Med. 58, 20–30 (2015)
Choi, R., et al.: Variability in plus disease identified using a deep learning-based retinopathy of prematurity severity scale. Ophthalmol. Retina 4(10), 1016–1021 (2020)
Yu, S., Xiao, D., Kanagasingan, Y.: Machine learning based automatic neovascularization detection on optic disc region. IEEE J. Biomed. Health Inform. 22(3), 886–894 (2018)
Welikala, R., et al.: Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification. Comput. Method Prog. Biomed. 114, 247–261 (2014)
Akram, M., Khalid, S., Tariq, A., Javed, M.: Detection of neovascularization in retinal images using multivariate m-Medios based classifier. Comput. Med. Imaging Graph. 37, 346–357 (2013)
Diabetic Retinopathy Detection-identify signs of diabetic retinopathy in eye images. https://www.kaggle.com/c/diabetic-retinopathy-detection/data. Accessed 18 Feb 2021
Simmonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: 3rd International Conference on Learning Representations, ICLR 2015, San Diego, USA (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Computer Vision and Pattern Recognition, Las Vegas, USA, pp. 770–778 (2016)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.: Densely connected convolutional networks. In: IEEE Computer Vision and Pattern Recognition, pp. 2261–2269 (2017)
Ran, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. In: Conference on Machine Learning, pp. 10692–10700 (2019)
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Carrillo-Gomez, C., Nakano, M., Gonzalez-H.Leon, A., Romo-Aguas, J.C., Quiroz-Mercado, H., Lopez-Garcia, O. (2021). Neovascularization Detection on Optic Disc Region Using Deep Learning. In: Roman-Rangel, E., Kuri-Morales, Á.F., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2021. Lecture Notes in Computer Science(), vol 12725. Springer, Cham. https://doi.org/10.1007/978-3-030-77004-4_11
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DOI: https://doi.org/10.1007/978-3-030-77004-4_11
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