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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

Comparison of error rate for the soft computing models.
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pone.0138493.g004: Comparison of error rate for the soft computing models.

Mentions: This section reports the results of the ELM bone age assessment models. Fig 3A shows the accuracy of the presented ELM BAA model. Subsequently, Fig 3B and 3C present the accuracy of the GP and ANN BAA models, respectively. It can be seen that most of the points fall along the diagonal line for the ELM assessment model. Consequently, the estimation results are in very good agreement with the measured values for the ELM model. This observation is supported by the very high coefficient of determination value. The number of either overestimated or underestimated values produced is limited. Thus, it is obvious that the estimated values exhibit high precision levels. Fig 4 shows the comparisons of error rates for the three soft computing models used in this study.


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

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

Comparison of error rate for the soft computing models.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0138493.g004: Comparison of error rate for the soft computing models.
Mentions: This section reports the results of the ELM bone age assessment models. Fig 3A shows the accuracy of the presented ELM BAA model. Subsequently, Fig 3B and 3C present the accuracy of the GP and ANN BAA models, respectively. It can be seen that most of the points fall along the diagonal line for the ELM assessment model. Consequently, the estimation results are in very good agreement with the measured values for the ELM model. This observation is supported by the very high coefficient of determination value. The number of either overestimated or underestimated values produced is limited. Thus, it is obvious that the estimated values exhibit high precision levels. Fig 4 shows the comparisons of error rates for the three soft computing models used in this study.

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