Open AccessMachine learning approach for detection of keratoconus
Author(s) -
S. Anita Shanthi,
K. Nirmaladevi,
M. Pyingkodi,
K. Dharanesh,
T. Gowthaman,
B. Harsavardan
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1055/1/012112
Subject(s) - keratoconus , subclinical infection , stage (stratigraphy) , ophthalmology , sensitivity (control systems) , refractive surgery , computer science , medicine , artificial intelligence , cornea , biology , engineering , electronic engineering , paleontology
Keratoconus is a progressive eye disease and it should be detected in early stage, to avert probable refractive surgery that could develop ecstasies. In this the authors proposes a new computer aided diagnosis model based on Support Vector Machine (SVM) learning to detect the early stage of keratoconus using the available topographic, pachymetric and aberrometry parameters of patients with keratoconus, subclinical keratoconus and normal corneas. The proposed SVM produces 91.8% accuracy with 94.2% sensitivity, 97.5% specificity for classification of early keratoconus from normal; 100% accuracy with 100%, 100% of sensitivity and specificity respectively for classification of early keratoconus from subclinical keratoconus.