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An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines.

Mansourvar M, Shamshirband S, Raj RG, Gunalan R, Mazinani I - PLoS ONE (2015)

Bottom Line: The estimation results of ELM models are compared with those of genetic programming (GP) and artificial neural networks (ANNs) models.The experimental results signify improvement in assessment accuracy over GP and ANN, while generalization capability is possible with the ELM approach.Moreover, the results are indicated that the ELM model developed can be used confidently in further work on formulating novel models of skeletal age assessment strategies.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, 50603, Malaysia.

ABSTRACT
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 (ELM) is designed and adapted to assess skeletal maturity. The main novelty of this approach is it overcomes the segmentation problem as suffered by existing systems. The estimation results of ELM models are compared with those of genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results signify improvement in assessment accuracy over GP and ANN, while generalization capability is possible with the ELM approach. Moreover, the results are indicated that the ELM model developed can be used confidently in further work on formulating novel models of skeletal age assessment strategies. According to the experimental results, the new presented method has the capacity to learn many hundreds of times faster than traditional learning methods and it has sufficient overall performance in many aspects. It has conclusively been found that applying ELM is particularly promising as an alternative method for evaluating skeletal age.

No MeSH data available.


Related in: MedlinePlus

Bone age result displayed with the gender and ethnicity features.
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pone.0138493.g002: Bone age result displayed with the gender and ethnicity features.

Mentions: The main step in implementing our BAA system is the process of estimating bone age according to the automated technique (Fig 2). Bone age is assessed by comparing a radiograph with samples from a repository that contains various ages for both genders and four different ethnicities. A temporary repository is needed to rank the retrieved radiographs. The tagged age values of the retrieved images are utilized as part of the BAA process and the final estimated age is calculated as the mean of the retrieved values:


An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines.

Mansourvar M, Shamshirband S, Raj RG, Gunalan R, Mazinani I - PLoS ONE (2015)

Bone age result displayed with the gender and ethnicity features.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4581666&req=5

pone.0138493.g002: Bone age result displayed with the gender and ethnicity features.
Mentions: The main step in implementing our BAA system is the process of estimating bone age according to the automated technique (Fig 2). Bone age is assessed by comparing a radiograph with samples from a repository that contains various ages for both genders and four different ethnicities. A temporary repository is needed to rank the retrieved radiographs. The tagged age values of the retrieved images are utilized as part of the BAA process and the final estimated age is calculated as the mean of the retrieved values:

Bottom Line: The estimation results of ELM models are compared with those of genetic programming (GP) and artificial neural networks (ANNs) models.The experimental results signify improvement in assessment accuracy over GP and ANN, while generalization capability is possible with the ELM approach.Moreover, the results are indicated that the ELM model developed can be used confidently in further work on formulating novel models of skeletal age assessment strategies.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, 50603, Malaysia.

ABSTRACT
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 (ELM) is designed and adapted to assess skeletal maturity. The main novelty of this approach is it overcomes the segmentation problem as suffered by existing systems. The estimation results of ELM models are compared with those of genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results signify improvement in assessment accuracy over GP and ANN, while generalization capability is possible with the ELM approach. Moreover, the results are indicated that the ELM model developed can be used confidently in further work on formulating novel models of skeletal age assessment strategies. According to the experimental results, the new presented method has the capacity to learn many hundreds of times faster than traditional learning methods and it has sufficient overall performance in many aspects. It has conclusively been found that applying ELM is particularly promising as an alternative method for evaluating skeletal age.

No MeSH data available.


Related in: MedlinePlus