Robust techniques for automated Bone Age Assessment

Thangam, P.; Saravanan, R.; Thanushkodi, K.

doi:10.1109/icctet.2013.6675918

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Cited by 2 publications

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“…TW method is more complex and performs better than the G&P method. Follow by TW approach, there are numerous other researches based on ROIs [24,9,3,23]. Although the outcome was improved, these traditional methods are still time-consuming and sensitive to the image quality.…”

Section: Related Workmentioning

confidence: 99%

Automatic Whole-body Bone Age Assessment Using Deep Hierarchical Features

Nguyen,

Kim

2019

Preprint

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Bone age assessment gives us evidence to analyze the children growth status and the rejuvenation involved chronological and biological ages. All the previous works consider left-hand X-ray image of a child in their works. In this paper, we carry out a study on estimating human age using whole-body bone CT images and a novel convolutional neural network. Our model with additional connections shows an effective way to generate a massive number of vital features while reducing overfitting influence on small training data in the medical image analysis research area. A dataset and a comparison with common deep architectures will be provided for future research in this field.

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Section: Related Workmentioning

confidence: 99%