Fully Automatic Model Based on SE-ResNet for Bone Age Assessment

• Published in: IEEE access Vol. 9; pp. 62460 - 62466
• Main Authors: He, Jin, Jiang, Dan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Age; Bone age assessment; Bones; convolutional neural network; deep learning; Feature extraction; Image coding; Image compression; Image segmentation; regression; Regression models; residual network; Task analysis; Training; X ray imagery; X-ray imaging
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3074713

Bone age assessment (BAA) based on hand X-ray imaging is a common clinical practice for investigating disorders and predicting the adult height of a child. However, the traditional manual method is time consuming and prone to obverse variability. There is an urgent need for a fully automatic framework based on deep learning with high performance and efficiency. We propose an end-to-end BAA model based on lossless image compression and a squeeze-and-excitation deep residual network (SE-ResNet). First, we apply the compression module to compress the raw image without losing important features. Second, the SE-ResNet-based model extracts features of the compressed images. Furthermore, the regression model with improved loss function predicts bone age. The experiments on a public dataset reveal that our method outperforms the baseline models. In conclusion, the presented method is a fully automatic and effective solution to process hand X-ray images for BAAs.