Prediction of bone age from an x-ray is one of the methods in the medical field to support predicting endocrine gland disease, growth abnormalities, and genetic disorders. A decision support system to predict the bone age from the x-ray image has been implemented. It utilizes traditional machine learning methods and deep learning. We propose the Region-Based Feature Connected Layer (RB-FCL) from the essential segmented region of hand x-ray. We treat the deep learning models as the feature extraction for each region of the hand x-ray bone. The Feature Connected Layers are the output from the trained important region, such as 1-radius-ulna, 2-carpal, 3-metacarpal, 4-phalanges, and 5-ephypisis. DenseNet121, InceptionV3, and InceptionResNetV2 are the deep learning models that we used to train the critical region. From the evaluation results, the Mean Absolute Error (MAE) results produced is 6.97. This result is better compared to standard deep learning models, which are 9.41.

中文翻译：

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用于骨骼年龄评估的深度学习模型的基于多区域的特征连接层（RB-FCL）

通过X射线预测骨龄是医学领域中支持预测内分泌腺疾病，生长异常和遗传疾病的方法之一。已经实现了从X射线图像预测骨骼年龄的决策支持系统。它利用传统的机器学习方法和深度学习。我们从手部X射线的基本分割区域中提出了基于区域的特征连接层（RB-FCL）。我们将深度学习模型视为手部X射线骨骼每个区域的特征提取。特征连接层是受过训练的重要区域的输出，例如1尺骨，2腕骨，3掌骨，4指骨和5骨赘。DenseNet121，InceptionV3和InceptionResNetV2是我们用来训练关键区域的深度学习模型。根据评估结果，产生的平均绝对误差（MAE）结果为6.97。与标准深度学习模型（9.41）相比，此结果更好。