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

Li, Kexin; Zhang, Jingzhe; Sun, Yueheng; Huang, Xinwang; Sun, Chunxue; Xie, Qiancheng; Cong, Shijie

doi:10.1109/access.2021.3108219

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…The goal of pooling layers, on the other hand, is to prevent over-fitting by lowering the number of convolutional layer features acquired. Finally, utilizing the final layer, the entire connected layers attempt to collect all of the descriptor features to be classified [25].…”

Section: Architecture Of the Proposed Deep Learningmentioning

confidence: 99%

Bone age assessment based on deep learning architecture

Jabbar

Abdulmunem²

2023

IJECE

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<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>

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Section: Architecture Of the Proposed Deep Learningmentioning

confidence: 99%

Bone age assessment based on deep learning architecture

Jabbar

Abdulmunem²

2023

IJECE

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“…A deep convolutional neural network (CNN) based on fine-grained image classification for automatic bone age assessment is proposed in Ref. [11] and achieves an accuracy of 66.38% for males and 68.63% for females, and the MAE are 3.71 ± 7.55 and 3.81 ± 7.74 months for males and females, respectively. By using the convolutional neural network (CNN), Ref.…”

Section: Introductionmentioning

confidence: 99%

An Unsupervised Deep-Learning Method for Bone Age Assessment

Zhu¹,

Nie²,

Hou³

et al. 2022

Preprint

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The bone age, reflecting the degree of development of the bones, can be used to predict the adult height and detect endocrine diseases of children. Both examinations of radiologists and variability of operators have a significant impact on bone age assessment. To decrease human intervention , machine learning algorithms are used to assess the bone age automatically. However, conventional supervised deep-learning methods need pre-labeled data. In this paper, based on the convolutional auto-encoder with constraints (CCAE), an unsupervised deep-learning model proposed in the classification of the fingerprint, we propose this model for the classification of the bone age and baptize it BA-CCAE. In the proposed BA-CCAE model, the key regions of the raw X-ray images of the bone age are encoded, yielding the latent vectors. The K-means clustering algorithm is used to obtain the final classifications by grouping the latent vectors of the bone images. A set of experiments on the Radiological Society of North America pediatric bone age dataset (RSNA) show that the accuracy of classifications at 48-month intervals is 76.15%. Although the accuracy now is lower than most of the existing supervised models, the proposed BA-CCAE model can establish the classification of bone age without any pre-labeled data, and to the best of our knowledge, the proposed BA-CCAE is one of the few trails using the unsupervised deep-learning method for the bone age assessment.

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“…The GP approach is mostly used because of its simplicity and compares the entire X-ray image with a standard reference atlas [1]. On the other hand, the TW method is more accurate with consideration of specific regions of interest from carpal and phalangeal joints and scores each region based on bone morphological features [5]. These two methods usually take considerable amounts of time and are subject to observer variability; two observers may report different scores, or even an observer may have different scores at different times.…”

mentioning

confidence: 99%

AE-BoNet: A Deep Learning Method for Pediatric Bone Age Estimation using an Unsupervised Pre- Trained Model

2023

J Biomed Phys Eng

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Background: Accurate bone age assessment is essential for determining the actual degree of development and indicating a disorder in growth. While clinical bone age assessment techniques are time-consuming and prone to inter/intra-observer variability, deep learning-based methods are used for automated bone age estimation.Objective: The current study aimed to develop an unsupervised pre-training approach for automatic bone age estimation, addressing the challenge of limited labeled data and unique features of radiographic images of hand bones. Bone age estimation is complex and usually requires more labeling data. On the other hand, there is no model trained with hand radiographic images, reused for bone age estimation. Material and Methods:In this fundamental-applied research, the collection of Radiological Society of North America (RSNA) X-ray image collection is used to evaluate the efficiency of the proposed bone age estimation method. An autoencoder is trained to reconstruct the original hand radiography images. Then, a model based on the trained encoder produces the final estimation of bone age.Results: Experimental results on the Radiological Society of North America (RSNA) X-ray image collection achieve a Mean Absolute Error (MAE) of 9.3 months, which is comparable to state-of-the-art methods. Conclusion:This study presents an approach to estimating bone age on hand radiographs utilizing unsupervised pre-training with an autoencoder and also highlights the significance of autoencoders and unsupervised learning as efficient substitutes for conventional techniques.

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

Cited by 8 publications

References 26 publications

Bone age assessment based on deep learning architecture

Bone age assessment based on deep learning architecture

An Unsupervised Deep-Learning Method for Bone Age Assessment

AE-BoNet: A Deep Learning Method for Pediatric Bone Age Estimation using an Unsupervised Pre- Trained Model

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