An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines.
Assessing skeletal age is a subjective and tedious examination process. Hence, automated assessment methods have been developed to replace manual evaluation in medical applications. In this study, a new fully automated method based on content-based image retrieval and using extreme learning machines...
Main Authors: | Marjan Mansourvar, Shahaboddin Shamshirband, Ram Gopal Raj, Roshan Gunalan, Iman Mazinani |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2015-01-01
|
Series: | PLoS ONE |
Online Access: | http://europepmc.org/articles/PMC4581666?pdf=render |
Similar Items
-
Estimation of Tsunami Bore Forces on a Coastal Bridge Using an Extreme Learning Machine
by: Iman Mazinani, et al.
Published: (2016-04-01) -
Automated Bone Age Assessment: Motivation, Taxonomies, and Challenges
by: Marjan Mansourvar, et al.
Published: (2013-01-01) -
Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions
by: Narjes Nabipour, et al.
Published: (2020-01-01) -
Prediction of significant wave height; comparison between nested grid numerical model, and machine learning models of artificial neural networks, extreme learning and support vector machines
by: Shahaboddin Shamshirband, et al.
Published: (2020-01-01) -
Machine Learning-Based Sentiment Analysis for Twitter Accounts
by: Ali Hasan, et al.
Published: (2018-02-01)