End-to-End Bone Age Assessment with Residual Learning

Souza, Daniel Henrique Mendes de; Oliveira, Manuel M.

doi:10.1109/sibgrapi.2018.00032

Cited by 9 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The procedure, known as BoneXpert, uses radiographs of the hand to mechanically recreate the borders of 15 bones. It then determines the "intrinsic" bone ages for each of 13 bones (radius, ulna, and 11 short bones [14].…”

Section: Literature Reviewmentioning

confidence: 99%

Age and gender classification with bone images using deep learning algorithms

Sundarasamy,

Kuttuva Rajendran

2023

IJEECS

View full text Add to dashboard Cite

In paediatrics, bone age is a crucial indicator of how a child's skeleton is developing. They have had great success ever since the creation of deep learning (DL)-based bone age prediction tools. Deep features learning, however, has a significant computing overhead problem. Deep convolution layers are used in this technique to learn representative features in the small yet useful regions that are extracted for feature learning. This work suggests using an extreme learning machine algorithm as the fundamental architecture in the final bone age assessment study to realise the rapid computation speed and feature interaction. The viability and efficacy of the suggested strategy have been verified by experiments using data that is openly accessible. The suggested model is explicitly trained using a cutting-edge end-to-end learning architecture employing bone scans to extract the most discriminative patches from the original high-resolution image. The bone picture is the foundation of the procedure. Our main objective is to categorize individuals by age using convolution neural network (CNN) classification models, such as the Xception and Mobile Net models. As a result, we have achieved results that are 90% and 94% accurate in classifying people by age using CNN models.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Age and gender classification with bone images using deep learning algorithms

Sundarasamy,

Kuttuva Rajendran

2023

IJEECS

View full text Add to dashboard Cite

show abstract

“…The ages of the bones were determined using their classification model the residual attention. Souza and Oliveira [21] provided residual learning as a method and inter the sex as input in the full section of their neural network. Wagner [22] to determine bone ages, researchers utilized both classification and regression approaches.…”

Section: Introductionmentioning

confidence: 99%

Bone age assessment based on deep learning architecture

Jabbar

Abdulmunem²

2023

IJECE

View full text Add to dashboard Cite

<span lang="EN-US">The fast advancement of technology has prompted the creation of automated systems in a variety of sectors, including medicine. One application is an automated bone age evaluation from left-hand X-ray pictures, which assists radiologists and pediatricians in making decisions about the growth status of youngsters. However, one of the most difficult aspects of establishing an automated system is selecting the best approach for producing effective and dependable predictions, especially when working with large amounts of data. As part of this work, we investigate the use of the convolutional neural networks (CNNs) model to classify the age of the bone. The work’s dataset is based on the radiological society of North America (RSNA) dataset. To address this issue, we developed and tested deep learning architecture for autonomous bone assessment, we design a new deep convolution network (DCNN) model. The assessment measures that use in this work are accuracy, recall, precision, and F-score. The proposed model achieves 97% test accuracy for bone age classification.</span>

show abstract

“…It is an end-to-end bone age prediction method, achieving an MAE of 0.79 years. Souza and Oliveira [26] proposed an end-to-end BAA method based on residual learning, achieving an MAE of 6.44 months. Ji et al [27] proposed a network called PRSNet.…”

Section: Introductionmentioning

confidence: 99%

Automatic Bone Age Assessment of Adolescents Based on Weakly-Supervised Deep Convolutional Neural Networks

Zhang

Sun

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Hand bone age, as the biological age of humans, can accurately reflect the development level and maturity of individuals. Bone age assessment results of adolescents can provide a theoretical basis for their growth and development and height prediction. In this study, a deep convolutional neural network (CNN) model based on fine-grained image classification is proposed, using a hand bone image dataset provided by the Radiological Society of North America (RSNA) as the research object. This model can automatically locate informative regions and extract local features in the process of hand bone image recognition, and then, the extracted local features are combined with global features of a complete image for bone age classification. This method can achieve end-to-end bone age assessment without any image annotation information (except bone age tags), improving the speed and accuracy of bone age assessment. Experimental results show that the proposed method achieves 66.38% and 68.63% recognition accuracy of males and females on the RSNA dataset, and the mean absolute errors are 3.71 ± 7.55 and 3.81 ± 7.74 months for males and females, respectively. The test time for each image is approximately 35 ms. This method achieves good performance and outperforms existing methods in bone age assessment based on weakly supervised fine-grained image classification.INDEX TERMS Bone age assessment, Deep learning, Convolutional neural network, Fine-grained image.

show abstract

End-to-End Bone Age Assessment with Residual Learning

Cited by 9 publications

References 26 publications

Age and gender classification with bone images using deep learning algorithms

Age and gender classification with bone images using deep learning algorithms

Bone age assessment based on deep learning architecture

Automatic Bone Age Assessment of Adolescents Based on Weakly-Supervised Deep Convolutional Neural Networks

Contact Info

Product

Resources

About