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.

A computer aided ophthalmic diagnosis system based on tomographic features / Bevilacqua, Vitoantonio; Simeone, Sergio; Brunetti, Antonio; Loconsole, Claudio; Trotta, Gianpaolo Francesco; Tramacere, Salvatore; Argentieri, Antonio; Ragni, Francesco; Criscenti, Giuseppe; Fornaro, Andrea; Mastronardi, Rosalina; Cassetta, Serena; D'Ippolito, Giuseppe (LECTURE NOTES IN COMPUTER SCIENCE). - In: Intelligent Computing Methodologies: 13th International Conference, ICIC 2017, Liverpool, UK, August 7-10, 2017: Proceedings. Part III / [a cura di] De-Shuang Huang, Abir Hussain, Kyungsook Han, M. Michael Gromiha. - Cham, CH : Springer, 2017. - ISBN 978-3-319-63314-5. - pp. 598-609 [10.1007/978-3-319-63315-2_52]

A computer aided ophthalmic diagnosis system based on tomographic features

Bevilacqua, Vitoantonio;Brunetti, Antonio;Loconsole, Claudio;Trotta, Gianpaolo Francesco;
2017-01-01

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.
2017
Intelligent Computing Methodologies: 13th International Conference, ICIC 2017, Liverpool, UK, August 7-10, 2017: Proceedings. Part III
978-3-319-63314-5
978-3-319-63315-2
Springer
A computer aided ophthalmic diagnosis system based on tomographic features / Bevilacqua, Vitoantonio; Simeone, Sergio; Brunetti, Antonio; Loconsole, Claudio; Trotta, Gianpaolo Francesco; Tramacere, Salvatore; Argentieri, Antonio; Ragni, Francesco; Criscenti, Giuseppe; Fornaro, Andrea; Mastronardi, Rosalina; Cassetta, Serena; D'Ippolito, Giuseppe (LECTURE NOTES IN COMPUTER SCIENCE). - In: Intelligent Computing Methodologies: 13th International Conference, ICIC 2017, Liverpool, UK, August 7-10, 2017: Proceedings. Part III / [a cura di] De-Shuang Huang, Abir Hussain, Kyungsook Han, M. Michael Gromiha. - Cham, CH : Springer, 2017. - ISBN 978-3-319-63314-5. - pp. 598-609 [10.1007/978-3-319-63315-2_52]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/112313
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