Abstract
Keratoconus is a bilateral progressive corneal disease characterized by thinning and apical protrusion; its early diagnosis is fundamental since it allows one to treat this rare disease by cross-linking approach, thus preventing a major corneal deformation and avoiding more invasive and risky surgical therapies, such as cornea transplant. Ophthalmology improvements have allowed a more rapid, precise and painless acquisition of corneal biometric parameters which are useful to evaluate alterations and abnormalities of eye’s outer structure. This paper presents a study about Keratoconus diagnosis based on a machine learning approach using corneal physical and morphological parameters obtained through Precisio™ tomographic examination. Artificial Neural Networks (ANNs) have been used for classification; in particular, a mono-objective Genetic Algorithm has been used to obtain the best topology for the neural classifiers for different input datasets obtained from features ranking. High levels of accuracy (higher than 90%) have been reached for all types of classification; in particular, binary classification has showed the best discrimination capability for Keratoconus identification.
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Bevilacqua, V. et al. (2017). A Computer Aided Ophthalmic Diagnosis System Based on Tomographic Features. In: Huang, DS., Hussain, A., Han, K., Gromiha, M. (eds) Intelligent Computing Methodologies. ICIC 2017. Lecture Notes in Computer Science(), vol 10363. Springer, Cham. https://doi.org/10.1007/978-3-319-63315-2_52
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DOI: https://doi.org/10.1007/978-3-319-63315-2_52
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