Abstract
The diagnosis of several ophthalmic diseases such as age-related macular degeneration, glaucoma, diabetic retinopathy and keratoconus involves the analysis of the eye topographic maps. The dependence between ophthalmology and images processing represents a point of attraction for researchers to benefit of capacity and performance of deep learning tools in image processing. These tools allow a better differentiation between a sick eye and a normal one based on the analysis of the eye topographic maps and can change potentially the practices of ophthalmologists in diagnosis and treatment of similar diseases. Among the diseases already mentioned, keratoconus, this non-inflammatory disease characterized by a progressive thinning of the cornea is often accompanied by aspens of vision. The increasing number of people diagnosed with keratoconus has made this disease the subject of several research studies.This paper represents an overview of artificial intelligence application in keratoconus classification and a proposal system of keratoconus classification based on neural networks.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Rahimy E (2018) Deep learning applications in ophthalmology. CurrOpinOphthalmol 29(3):254–260
Balyen L, Peto T (2019) Promising artificial intelligence–machine learning–deep learning algorithms in ophthalmology. Asia-Pacific Journal of Ophthalmology 8(3):264–272
Ting DSW, Pasquale LR et al (2019) Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol 103(2):167–175
Yousefi S et al (2018) Detection of longitudinal visual field progression in glaucoma using machine learning. Am J Ophthalmol 193:71–79
Gargeya R, Leng T (2017) Automated identification of diabetic retinopathy using deep learning. Ophthalmology 124(7):962–969
Kamiya K, Ayatsuka Y et al (2019) Keratoconus detection using deep learning of colour-coded maps with anterior segment optical coherence tomography: a diagnostic accuracy study. BMJ Open 9(9):1–7
Grewal PS, Oloumi F et al (2018) Deep learning in ophthalmology: a review. Can J Ophthalmol 53(4):309–313
Lin SR, Ladas JG et al (2019) A review of machine learning techniques for keratoconus detection and refractive surgery screening. Seminars in Ophthalmology 34(4):317–326
Deo RC (2015) Machine learning in medicine. Circulation 132(20):1920–1930
Sainath TN, Mohamed AR, et al (2013) Deep CNN for LVCSR. In: 2013 International conference on acoustics, speech and signal processing, pp 8614–8618. IEEE, Vancouver
Lavric A, Valentin P (2019) KeratoDetect: keratoconus detection algorithm using convolutional neural networks. Computational Intelligence and Neuroscience 2019:1–9
Issarti I, Consejo A et al (2019) Computer aided diagnosis for suspect keratoconus detection. Comput Biol Med 109(January):33–42
Salem BR, Solodovnikov VI (2019) Decision support system for an early-stage keratoconus diagnosis. J Phys Conf Ser 1419 (2019)
Yousefi E, Id HT et al (2018) Keratoconus severity identification using unsupervised machine learning. PLoS ONE 13(11):1–11
Castro-luna GM et al (2019) Contact lens and anterior eye robust keratoconus detection with bayesian network classifier for placido-based corneal indices. Contact Lens and Anterior Eye, Available online 20 Dec 2019
Hidalgo IR, Gatinel D et al (2017) Validation of an objective keratoconus detection system implemented in a ScheimpflugTomographer and comparison with other methods. Cornea 36(6):689–695
Ali AH, Ghaeb NH, Musa ZM (2017) Support vector machine for keratoconus detection by using topographic maps with the help of image processing techniques. IOSR-JPBS 12(6):50–58
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Mustapha, A., Mohamed, L., Ali, K. (2022). Keratoconus Classification Using Machine Learning. In: Bennani, S., Lakhrissi, Y., Khaissidi, G., Mansouri, A., Khamlichi, Y. (eds) WITS 2020. Lecture Notes in Electrical Engineering, vol 745. Springer, Singapore. https://doi.org/10.1007/978-981-33-6893-4_25
Download citation
DOI: https://doi.org/10.1007/978-981-33-6893-4_25
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-6892-7
Online ISBN: 978-981-33-6893-4
eBook Packages: EngineeringEngineering (R0)