Abstract
Keratoconus refers to the condition of the eye, where the corneal thinning occurs accompanied by a change in the corneal shape. A cone shaped protrusion occurs in the cornea and hence the name keratoconus. This manifests in the forms of vision problems. Treatment of the Keratoconus ranges from contact lenses to corneal transplants. Detection of Keratoconus at earlier stages is important to prevent visual repair or vision loss or costly treatments. Detection of keratoconus is being done by clinical signs including Fleischer’s rings, Munson’s sign, Rizutti’s sign, Vogt’s Striae and Hydrops. Advanced techniques like Keratoscope and Videokeratoscope are being used for diagnosis of Keratoconus. With the advent of machine learning, new techniques including multilayer perceptron, Radial Basis Function Neural Network (RBFNN), Decision Trees and Convolutional Neural Network (CNN) are being developed in the automatic detection and screening of various disorders. This survey paper reviews the machine learning approaches in diagnosis of Keratoconus.
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Subramanian, P., Ramesh, G.P., Parameshachari, B.D. (2022). Comparative Analysis of Machine Learning Approaches for the Early Diagnosis of Keratoconus. In: Majhi, S., Prado, R.P.d., Dasanapura Nanjundaiah, C. (eds) Distributed Computing and Optimization Techniques. Lecture Notes in Electrical Engineering, vol 903. Springer, Singapore. https://doi.org/10.1007/978-981-19-2281-7_23
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