卷积神经网络
预处理器
骨龄
深度学习
人工智能
计算机科学
放射性武器
机器学习
模式识别(心理学)
医学
放射科
解剖
作者
Achouak Zerari,Oussama Djedidi,Laïd Kahloul,Romeo Carlo,Ikram Remadna
出处
期刊:Lecture notes in networks and systems
日期:2022-01-01
卷期号:: 373-383
被引量:1
标识
DOI:10.1007/978-3-031-12097-8_32
摘要
Bone age assessments are methods that doctors use in pediatric medicine. They are used to assess the growth of children by analyzing X-ray images. This work focuses on the development of a deep learning model to estimate from X-ray images. Such a model would avoid the fallacies of subjective methods and raise the accuracy of the assessment. In our work, the model is based on convolutional neural networks (CNN) and is composed of two steps: a preprocessing step generating image masks, and a prediction step that uses these masks to generate the assessment. The model is trained and tested using a public Radiological Society of North America(RSNA) bone age dataset. Finally, experimental results demonstrate the effectiveness of the proposed approach compared to similar works in the literature.
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