论文标题
使用深度学习模型评估小儿骨龄段评估
Pediatric Bone Age Assessment using Deep Learning Models
论文作者
论文摘要
骨骼年龄评估（BAA）是确定骨骼和年代年龄之间年龄差异的标准方法。手动流程很复杂，需要专家的专业知识。这是深度学习发挥作用的地方。在这项研究中，使用VGG-16，InceptionV3，XceptionNet和Mobilenet等预训练的模型来评估输入数据的骨骼年龄，并比较并评估其平均平均误差并评估哪种模型可以预测最佳。
Bone age assessment (BAA) is a standard method for determining the age difference between skeletal and chronological age. Manual processes are complicated and necessitate the expertise of experts. This is where deep learning comes into play. In this study, pre-trained models like VGG-16, InceptionV3, XceptionNet, and MobileNet are used to assess the bone age of the input data, and their mean average errors are compared and evaluated to see which model predicts the best.
