ASSESSING THE PERFORMANCE OF CATARACT NET AND OTHER DEEP LEARNING SYSTEMS FOR AUTOMATED CATARACT DETECTION

Miss Pragya Shrivastava; Chandra Shekhar Gautam; Sajal Kumar Kar

doi:10.29121/shodhkosh.v5.i5.2024.3612

Authors

Miss Pragya Shrivastava Department of Computer Science and Engineering AKS University SATNA
Dr. Chandra Shekhar Gautam Department of Computer Science and Engineering AKS University SATNA
Sajal Kumar Kar Department of Computer Science and Engineering AKS University SATNA

DOI:

https://doi.org/10.29121/shodhkosh.v5.i5.2024.3612

Keywords:

Machine Learning, Deep Learning, Convolutional Neural Network (CNN), ResNet

Abstract [English]

The normal lens of the eye, which is located behind the iris and pupil, becomes clouded when a cataract develops. Normally clean, the lens aids in focusing light onto the retina, enabling sharp vision. The formation of a cataract results in an opaque or clouded lens, which distorts or blurs vision. Although aging is frequently linked to cataract development, additional causes include heredity, trauma, certain drugs, or underlying medical disorders like diabetes. The usual symptoms are progressive loss of vision, heightened susceptibility to light, blurred or yellowed colors, and difficulties seeing at night. A thorough eye exam that includes slit-lamp and visual acuity tests is typically used to diagnose cataracts.

References

Lavric, Alexandru, et al. "Detecting keratoconus from corneal imaging data using machine learning." IEEE Access 8 (2020): 149113-149121. DOI: https://doi.org/10.1109/ACCESS.2020.3016060

Hidalgo, Irene Ruiz, et al. "Evaluation of a machine-learning classifier for keratoconus detection based on Scheimpflug tomography." Cornea 35.6 (2016): 827-832. DOI: https://doi.org/10.1097/ICO.0000000000000834

Shanthi, S., et al. "Machine learning approach for detection of keratoconus." IOP Conference Series: Materials Science and Engineering. Vol. 1055. No. 1. IOP Publishing, 2021. DOI: https://doi.org/10.1088/1757-899X/1055/1/012112

Lavric, Alexandru, and Popa Valentin. "KeratoDetect: keratoconus detection algorithm using convolutional neural networks." Computational intelligence and neuroscience 2019 (2019). DOI: https://doi.org/10.1155/2019/8162567

Cohen, Eyal, et al. "Use of machine learning to achieve keratoconus detection skills of a corneal expert." International Ophthalmology 42.12 (2022): 3837-3847. DOI: https://doi.org/10.1007/s10792-022-02404-4

Cao, Ke, et al. "Accuracy of machine learning assisted detection of keratoconus: a systematic review and meta-analysis." Journal of Clinical Medicine 11.3 (2022): 478. DOI: https://doi.org/10.3390/jcm11030478

Yoo, Tae Keun, et al. "Adopting machine learning to automatically identify candidate patients for corneal refractive surgery." NPJ digital medicine 2.1 (2019): 59. DOI: https://doi.org/10.1038/s41746-019-0135-8

Brás, Nuno Miguel Ferreira Vivas. "Characterization and diagnostics of corneal transparency by OCT imaging and machine learning." (2023).

Panda, Saroj Kailash, and Nikhil Panjwani. "Cataract Detection Using Deep Learning." (2023) DOI: https://doi.org/10.21203/rs.3.rs-3178940/v1

Khan, Md Sajjad Mahmud, et al. "Cataract detection using convolutional neural network with VGG-19 model." 2021 IEEE World AI IoT Congress (AIIoT). IEEE, 2021

M. S. Junayed, A. N. M. Sakib, N. Anjum, M. B. Islam, and A. A. Jeny, EczemaNet: A deep CNN-based eczema diseases classi cation, in Proc. IEEE 4th Int. Conf. Image Process., Appl. Syst. (IPAS), Dec. 2020, pp. 174179. DOI: https://doi.org/10.1109/IPAS50080.2020.9334929

J.-Y. Hung, C. Perera, K.-W. Chen, D. Myung, H.-K. Chiu, C.-S. Fuh, C.-R. Hsu, S.-L. Liao, and A. L. Kossler, A deep learning approach to identify blepharoptosis by convolutional neural networks, Int. J. Med. Informat., vol. 148, Apr. 2021, Art. no. 104402. DOI: https://doi.org/10.1016/j.ijmedinf.2021.104402

Ocular Disease Recognition, Dataset, https://www.kaggle.com/andrewmvd/ocular-disease recognition-odir5k.

J. Staal, M. D. Abràmoff, M. Niemeijer, M. A. Viergever, and B. van Ginneken, ‘Ridge-based vessel segmentation in color images of the retina,’ IEEE Trans. Med. Imag., vol. 23, no. 4, pp. 501–509, Apr. 2004.

A. Budai, R. Bock, A. Maier, J. Hornegger, and G. Michelson, ‘Robust vessel segmentation in fundus images,’ Int. J. Biomed. Imag., vol. 2013, pp. 1–11, Dec. 2013. DOI: https://doi.org/10.1155/2013/154860

Z. Zhang, F. S. Yin, J. Liu, W. K. Wong, N. M. Tan, B. H. Lee, J. Cheng, and T. Y. Wong, ORIGA-light: An online retinal fundus image database for glaucoma analysis and research, in Proc. Annu. Int. Conf. IEEE Eng. Med. Biol., Aug. 2010, pp. 30653068.

P. Porwal, S. Pachade, R. Kamble, M. Kokare, G. Deshmukh, V. Sahasrabuddhe, and F. Meriaudeau, Indian diabetic retinopathy image dataset (IDRiD): A database for diabetic retinopathy screening research, Data, vol. 3, no. 3, p. 25, Sep. 2018. DOI: https://doi.org/10.3390/data3030025

C. Hernandez-Matas, X. Zabulis, A. Triantafyllou, P. Anyfanti, S. Douma, and A. A. Argyros, FIRE: Fundus image registration dataset, Model. Artif. Intell. Ophthalmol., vol. 1, no. 4, pp. 1628, 2017. DOI: https://doi.org/10.35119/maio.v1i4.42

J. Staal, M. D. Abràmoff, M. Niemeijer, M. A. Viergever, and B. van Ginneken, Ridge-based vessel segmentation in color images of the retina, IEEE Trans. Med. Imag., vol. 23, no. 4, pp. 501509, Apr. 2004. DOI: https://doi.org/10.1109/TMI.2004.825627