An Ensemble-Based Densely-Connected Deep Learning System for Assessment of Skeletal Maturity

Assessment of skeletal maturity is important for a clinician to make a decision of the most appropriate treatment on various skeletal disorders. This task is very challenging when using machine learning method due to the limited data and large anatomical variations among different subjects. In this article, we propose an ensemble-based deep learning pipeline to automatically assess the distal radius and ulna (DRU) maturity from left-hand radiographs. At the same time, we adapted the concept of densely connected mechanism in the proposed network architecture to reuse features and prevent gradient disappearance. Therefore, the model acquires two convincing advantages: first, our model preserves the maximum information flow and has a much faster convergence rate. Second, our model avoids overfitting even if training with limited data. The experimental dataset contains 1189 left-hand
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-ray scans of children and teenagers. The proposed method achieves 85.27% and 91.68% for radius and ulna classification, respectively. Extensive experiments prove that our model performs better than using other network structures.