Pediatric Bone Age Prediction Using Deep Learning
Computer Vision and Pattern RecognitionArtificial IntelligenceMachine Learning
Summary
The authors developed a computer program using deep learning to estimate children's bone age from X-ray images, which can help doctors understand a child's growth. They used a smart type of neural network called EfficientNet, with one version enhanced by an attention mechanism, to analyze over 12,000 hand X-rays. Their tests showed that the bigger and attention-enhanced models predicted bone age more accurately compared to the simpler one. The training process was successful without mistakes like overfitting, showing that their approach can aid in diagnosing growth-related health issues in children.
Authors
Al Zadid Sultan Bin Habib, Md. Ekramul Islam, Md Asif Bin Syed, Md Younus Ahamed, Tanpia Tasnim
Abstract
Pediatric bone age prediction is a crucial task in clinical practice that can help diagnose endocrine disorders and provide insight into a child's growth and development. However, conventional bone age prediction methods are often labor-intensive and require specialized radiological expertise. This paper presents a Deep Learning (DL)-based approach to pediatric bone age prediction using EfficientNet with Additive Attention, a state-of-the-art neural network architecture for image classification and regression tasks. The method utilizes over 12,000 X-ray images from the RSNA bone age dataset. It involves image preprocessing, transforming them into three-channel images, and training a Convolutional Neural Network (CNN) to automatically learn the features of hand bone images. This approach provides a more effective and accurate solution for predicting bone age, which is critical in diagnosing pediatric endocrine diseases. This work uses two variations of the EfficientNet model (B0 and B4), where EfficientNetB4 is also finetuned with the Additive Attention mechanism. These three models predict the age for the original age, and their comparison is shown in curves. The predicted ages depict that in most cases, EfficientNetB4 and EfficientNetB4 with Additive Attention (EN-AA) successfully predicted the bone ages more accurately regarding the original age, and their performance was better than the EfficientNetB0. Specific performance metrics are provided to underscore this improvement. Learning curves for training and validation loss confirm effective learning without overfitting or underfitting, further validating our approach's efficacy in pediatric endocrine disease diagnosis.