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A Computer Aided Ophthalmic Diagnosis System Based on Tomographic Features

  • Vitoantonio Bevilacqua
  • Sergio Simeone
  • Antonio Brunetti
  • Claudio Loconsole
  • Gianpaolo Francesco Trotta
  • Salvatore Tramacere
  • Antonio Argentieri
  • Francesco Ragni
  • Giuseppe Criscenti
  • Andrea Fornaro
  • Rosalina Mastronardi
  • Serena Cassetta
  • Giuseppe D’Ippolito
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10363)

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.

Keywords

Keratoconus Rare disease Corneal Tomography Artificial Neural Networks Genetic algorithm Decision support systems 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Vitoantonio Bevilacqua
    • 1
  • Sergio Simeone
    • 1
  • Antonio Brunetti
    • 1
  • Claudio Loconsole
    • 1
  • Gianpaolo Francesco Trotta
    • 2
  • Salvatore Tramacere
    • 3
  • Antonio Argentieri
    • 3
  • Francesco Ragni
    • 3
  • Giuseppe Criscenti
    • 3
  • Andrea Fornaro
    • 3
  • Rosalina Mastronardi
    • 3
  • Serena Cassetta
    • 3
  • Giuseppe D’Ippolito
    • 3
  1. 1.Department of Electrical and Information Engineering (DEI)Polytechnic University of BariBariItaly
  2. 2.Department of Mechanics, Mathematics and Management (DMMM)Polytechnic University of BariBariItaly
  3. 3.Ligi Tecnologie MedicaliTarantoItaly

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