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Apr 24, 2020 · Using a total of eleven parameters, random forest, support vector machine and k-nearest neighbors had better performance in detecting ...
Aug 21, 2024 · This paper provides an in-depth examination of eight Machine Learning (ML) algorithms commonly employed in diagnosing keratoconus (KC) over the past decade.
Machine learning has the potential to improve the detection of subclinical keratoconus or early keratoconus in routine ophthalmic practice. Currently, there is ...
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Pentacam is the most commonly used machine learning method, whereas the NN is the AI algorithm frequently used to help find a more accurate diagnosis. The AI ...
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However, the performance of machine learning models in distinguishing early KC eyes from controls was poorer, with the maximum pooled sensitivity of 0.88.
Purpose : To determine the sets of 4 keratometric parameters, that best classify patients as keratoconus vs normal, as well as, which of 4 machine learning ...
The developed ML model can provide better performance in diagnosis compared to other models in the literature.
Feb 9, 2024 · Performance Comparison of Machine Learning Algorithms for Keratoconus Detection · Keratoconus Severity Detection From Elevation, Topography and ...
As a first use of machine learning approach with ORA parameters, this research work presents a performance comparison of the main machine learning algorithms.
performance of various machine learning algorithms to detect subclinical ... “KeratoDetect: keratoconus detection algorithm using convolutional neural ...