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2023 | OriginalPaper | Chapter

A Fundus Image Myopia Diagnosis Model Based on Homogeneous Multimodal Feature Fusion

Authors : Peng- Ceng Wen, Yu Guan, Jian- Qiang Li, Tariq Mahmood, Yin-Zheng Zhao

Published in: Frontier Computing

Publisher: Springer Nature Singapore

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Abstract

High myopia is one of the leading causes of fundus diseases. If it can be found and treated in time, the risk of fundus lesions in children's growth will be reduced, and the growth rate of patients with visual disabilities will be effectively controlled. Vascular, as one of the significant features in fundus images, are often used as additional elements to help ophthalmologists diagnose. Hence, in this paper, based on the idea of homogeneous multimodality, we design a neural network model with two branches that simultaneously processing the vascular feature image and original fundus image therefore to automatic detect high myopia based on the fundus images, and hope to make a great difference in clinical practice. Extensive comparative experiments were conducted between our method and other general classification models through a private retinal fundus data set. The results show that our method achieves the best performance of 93.4% in accuracy.

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W. H. Organization and Others. World report on vision (2019)

Ikuno, Y.: Overview of the complications of high myopia. Retina 37, 2347–2351 (2017)CrossRef

Liu, L., et al.: Prediction of premyopia and myopia in Chinese preschool children: a longitudinal cohort. BMC Ophthalmol. 21, 1–10 (2021)CrossRef

Kapoor, R., Walters, S.P., Al-Aswad, L.A.: The current state of artificial intelligence in ophthalmology. Surv. Ophthalmol. 64(2), 233–240 (2019). https://doi.org/10.1016/j.survophthal.2018.09.002CrossRef

Lee, J., et al.: Deep learning in medical imaging: general overview. Korean J. Radiol. 18, 570–584 (2017)CrossRef

Huang, S., Pareek, A., Seyyedi, S., Banerjee, I., Lungren, M.P.: Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines. NPJ Digital Med. 3, 1–9 (2020)

Jonas, R.A., Yan, Y.N., Zhang, Q., Wang, Y.X., Jonas, J.B.: Elongation of the disc-fovea distance and retinal vessel straightening in high myopia in a 10-year follow-up of the Beijing eye study. Sci. Rep. 11, 1–8 (2021)CrossRef

Shin, J.W., Kwon, J., Lee, J., Kook, M.S.: Relationship between vessel density and visual field sensitivity in glaucomatous eyes with high myopia. Br. J. Ophthalmol. 103, 585–591 (2019)CrossRef

Varadarajan, V., et al.: Deep learning for predicting refractive error from retinal fundus images. Investig. Ophthalmol. Visual Sci. 59, 2861–2868 (2018)

10.

Shi, Z., Wang, T., Huang, Z., Xie, F., Song, G.: A method for the automatic detection of myopia in Optos fundus images based on deep learning. Int. J. Numeric. Methods Biomed. Eng. 37, e3460 (2021)MathSciNet

11.

Ting, D.S.J., et al.: Artificial intelligence for anterior segment diseases: emerging applications in ophthalmology. Br. J. Ophthalmol. 105, 158–168 (2021)CrossRef

12.

Hemalakshmi, G.R., Santhi, D., Mani, V.R.S., Geetha, A., Prakash, N.B.: Classification of retinal fundus image using MS-DRLBP features and CNN-RBF classifier. J. Ambient. Intell. Humaniz. Comput. 12(9), 8747–8762 (2020). https://doi.org/10.1007/s12652-020-02647-yCrossRef

13.

Musavi, M.T., Ahmed, W., Chan, K.H., Faris, K.B., Hummels, D.M.: On the training of radial basis function classifiers. Neural Netw. 5, 595–603 (1992)CrossRef

14.

Touvron, H., Cord, M., Sablayrolles, A., Synnaeve, G., Gou, H.E.J.E.: Going deeper with image transformers, pp. 32–42 (2021)

15.

Yu, G.H., Sun, Z.L., Sedra, D., Weinberger, K.Q.: Deep networks with stochastic depth. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision – ECCV 2016. LNCS, vol. 9908, pp. 646–661. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_39CrossRef

16.

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition, pp. 770–778 (2016)

17.

Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)

18.

Hendrycks, D., Gimpel, K.: Gaussian error linear units (GELUS). arXiv preprint arXiv:1606.08415 (2016)

19.

Liu, Z., Mao, H., Wu, C., Feichtenhofer, C., Darrell, T., Xie, S.: A ConvNet for the 2020s. arXiv preprint arXiv:2201.03545 (2022)

20.

Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: PMLR, pp. 448–456 (2015)

21.

Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks, pp. 4700–4708 (2017)

22.

Ma, N., Zhang, X., Zheng, H.-T., Sun, J.: Shufflenet v2: practical guidelines for efficient CNN architecture design. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 122–138. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01264-9_8CrossRef

23.

Reid, J.E., Eaton, E.: Artificial intelligence for pediatric ophthalmology. Curr. Opin. Ophthalmol. 30, 337–346 (2019)CrossRef

24.

Tan, M., Le, Q.: Efficientnetv2: smaller models and faster training. In: PMLR, pp. 10096–10106 (2021)

25.

Dosovitskiy, L., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)

26.

Liu, Z., et al.: Swin transformer: Hierarchical vision transformer using shifted windows, pp. 10012–10022 (2021)

Title: A Fundus Image Myopia Diagnosis Model Based on Homogeneous Multimodal Feature Fusion
Authors: Peng- Ceng Wen
Yu Guan
Jian- Qiang Li
Tariq Mahmood
Yin-Zheng Zhao
Publisher: Springer Nature Singapore
Book: Frontier Computing
Print ISBN: 978-981-9914-27-2

Electronic ISBN: 978-981-9914-28-9

Copyright Year: 2023
DOI: https://doi.org/10.1007/978-981-99-1428-9_5

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