Bone Age Assessment Based on Improved Deep Residual Networks
Authors: 
Yanna Liao, Tao ZhangAuthors Info & Claims
Yanna Liao, Tao ZhangAuthors Info & ClaimsPages 623 - 630
Abstract
Bone age plays an important role in the scene of pediatrics and judicial identification, because the traditional bone age assessment is time-consuming, laborious and depends on the experience of physicians, the results of artificial bone age assessment will vary from person to person. This paper has collected the X-ray image from a class A tertiary children's hospital, and presents the convolutional neural network suitable for China 05 bone age assessment. For the collected Chinese 3-16-year-old youth hand bone image, using the stacked denoising autoencoder (SDAE) combined with ResNet50, while reducing the noise of soft tissue and effectively improving the feature extraction ability of the model; Secondly, the 3×3 convolution in the ResNet50 residual block is replaced with a pyramid split attention (PSA) module to get the new model, fusion multi-level features of space and channel attention, adapt to re-define features; Presents the adaptive dual-channel pooling layer by combining the max pooling and average pooling; Use pre-excitement to speed up convergence and label smooth loss function to prevent the model from overfitting, and finally establish a deep learning classification model for China 05 bone age assessment. The experimental results show that the accuracy of ±1 year in this method reaches 93.22% of men, and 91.71% of women. The Mean Absolute Error (MAE) also decreases.
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Index Terms
- Bone Age Assessment Based on Improved Deep Residual Networks
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Published In
September 2022
1221 pages
ISBN:9781450396899
DOI:10.1145/3573942
Copyright © 2022 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 16 May 2023
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AIPR 2022
AIPR 2022: 2022 5th International Conference on Artificial Intelligence and Pattern Recognition
September 23 - 25, 2022
Xiamen, China
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