Bone Age Prediction using a Convolutional Neural Network-based Regression Algorithm employing Attention-Directing and Cluster

Abstract

Bone age assessment (BAA) is a critical research topic in pediatric radiology, with growing interest in developing automated BAA methods. This study proposes a bone age prediction model integrating cluster analysis and convolutional neural network (CNN) regression, further enhanced by a multi-scale attention mechanism to construct a "divide-and-focus" dual-driven deep learning framework. Targeting age-sensitive regional features in hand radiographs, we innovatively design an adaptive spatial attention module that achieves hierarchical anatomical feature enhancement through saliency detection of attention-guided regions of interest (ROI). The algorithm first uses multiconstrained clustering of K methods to generate age-specific subsets, followed by parallel execution on each subset: 1) attention-guided ROI segmentation and feature enhancement; 2) validation of the base CNN regression networks (including ResNet, DenseNet and EfficientNetV2); 3) set of cross-subset models with Bayesian-optimized weighting strategies for final prediction. By synergistically integrating the data distribution priors with attention-driven anatomical priors, the method delivers interpretable solutions when performing medical image regression tasks. The modular design ensures compatibility with mainstream CNN architectures. The method will aid pediatric growth monitoring and the diagnosis of endocrine disorders.

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