An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines

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.

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“…The Editor-in-Chief has retracted this article (Toghroli et al 2018) because validity of the content of this article cannot be verified. This article showed evidence of substantial text overlap (most notably with the articles cited Cojbasic et al 2016;Mazinani et al 2016;Mohammadian et al 2016;Mansourvar et al 2015) and authorship manipulation. Meldi Suhatril, Zainah Ibrahim, Maryam Safa, Mahdi Shariati and Shahaboddin Shamshirband do not agree to this retraction.…”

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