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.
|Titolo:||A computer aided ophthalmic diagnosis system based on tomographic features|
|Titolo del libro:||Intelligent Computing Methodologies: 13th International Conference, ICIC 2017, Liverpool, UK, August 7-10, 2017: Proceedings. Part III|
|Data di pubblicazione:||2017|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1007/978-3-319-63315-2_52|
|Appare nelle tipologie:||2.1 Contributo in volume (Capitolo o Saggio)|